Energy_Mgmt_Regression.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Title: Optimal Regression Model Selection"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Abstract\n",
    "\n",
    "This project is aimed to determine the most suitable _Regression model_ for a given dataset, with the goal of optimizing predictive accuracy. The methodology involved an exhaustive exploration of various regression models, including: \n",
    "1. Linear, 2. Decision Tree, 3. Random Forest, 4. Ridge, 5. Lasso, and 6. Support Vector Regression (SVR). \n",
    "\n",
    "The dataset was carefully preprocessed and trained, and evaluation metrics:\n",
    "1. R-squared, 2. Mean Squared Error (MSE), 3. Root Mean Squared Error (RMSE), and 4. Mean Absolute Error (MAE) \n",
    "\n",
    "were employed to quantitatively compare model performance.\n",
    "\n",
    "Rigorous feature selection methods:\n",
    "1. Filter techniques - A. Feature Importance, B. Variance Threshold, C. SelectKBest using ANOVA F-value, D. SelectPercentile\n",
    "2. Wrapper Technique - A. Backward selection, B. RFE (Backward selection method), C. RFE with Cross-Validation (Method 1), D. RFE with Cross-Validation (Method 2)\n",
    "\n",
    "were also utilised to find most important features.\n",
    "\n",
    "Feature selection procedures also highlighted the importance of specific predictor variables in enhancing model accuracy. These results have significant implications for decision-making processes reliant on accurate predictions, spanning fields such as finance, economics, and engineering.\n",
    "\n",
    "The study's outcomes underscore the critical role of model selection in achieving optimal predictions, enabling stakeholders to make informed choices based on robust data-driven insights. The methodologies and insights gleaned from this study provide a valuable framework for selecting regression models tailored to specific datasets, contributing to the advancement of accurate and reliable predictive modeling."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Introduction\n",
    "\n",
    "Regression model selection plays a pivotal role in data analysis by determining the most appropriate mathematical framework for modeling relationships between variables. It holds profound significance due to its potential to enhance the accuracy, interpretability, and generalization capabilities of predictive models.\n",
    "\n",
    "The core essence of regression analysis lies in its ability to unveil hidden patterns, quantify associations, and make informed predictions. However, the effectiveness of these outcomes is directly linked to the selection of the right regression model. A misaligned choice can lead to biased estimates, unreliable predictions, and flawed insights.\n",
    "\n",
    "A key aspect of regression model selection is striking a balance between model complexity and generalization. Overly complex models may overfit the training data, capturing noise and leading to poor performance on new data. On the other hand, overly simplistic models may overlook important relationships, resulting in underfitting.\n",
    "\n",
    "Furthermore, different regression techniques cater to distinct types of relationships and data structures. For instance, linear regression assumes a linear relationship between variables, while advanced methods like ridge and lasso regression are designed to handle multicollinearity and prevent overfitting. The relevance of regression model selection extends across diverse fields. In economics, it aids in predicting market trends and consumer behavior. In healthcare, it supports disease prognosis and treatment effectiveness assessment. Environmental science benefits from modeling complex interactions. In all domains, informed model selection translates to more accurate insights, better decision-making, and enhanced problem-solving capabilities.\n",
    "\n",
    "In conclusion, the careful selection of a regression model is a cornerstone of effective data analysis. It ensures the model's compatibility with the data's nature, enhances predictive accuracy, and amplifies the potential for extracting meaningful insights. As the volume and complexity of data continue to grow, the role of regression model selection becomes increasingly pivotal in unlocking the true value of data-driven exploration and decision-making."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First of all we will import necessary libraries."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import linear_model \n",
    "from sklearn import metrics \n",
    "from sklearn.model_selection import train_test_split\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The dataset's preparation for analysis entails a meticulous process involving data sources, variables, and preprocessing steps to ensure its reliability and relevance. Ensuring data quality and integrity is crucial to derive accurate insights."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Cleaning and Preprocessing\n",
    "\n",
    "Eliminate inconsistencies, missing values, and outliers that could distort analysis results. Impute missing data using appropriate methods. Here, I have removed the inconsistencies (missing values) and created a new file for cleaned data. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(8480, 107)\n"
     ]
    },
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       "      <th></th>\n",
       "      <th>UTC</th>\n",
       "      <th>UTCTimeZone</th>\n",
       "      <th>Time</th>\n",
       "      <th>Avg IL1 (A)</th>\n",
       "      <th>Avg IL2 (A)</th>\n",
       "      <th>Avg IL3 (A)</th>\n",
       "      <th>Avg INe (A)</th>\n",
       "      <th>Avg Phase-to-phase voltages U12 (V)</th>\n",
       "      <th>Avg Phase-to-phase voltages U23 (V)</th>\n",
       "      <th>Avg Phase-to-phase voltages U31 (V)</th>\n",
       "      <th>...</th>\n",
       "      <th>Avg OutV1 (V)</th>\n",
       "      <th>Max OutV1 (V)</th>\n",
       "      <th>Min OutV1 (V)</th>\n",
       "      <th>Avg OutV2 (V)</th>\n",
       "      <th>Max OutV2 (V)</th>\n",
       "      <th>Min OutV2 (V)</th>\n",
       "      <th>Avg OutV3 (V)</th>\n",
       "      <th>Max OutV3 (V)</th>\n",
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       "      <th>0</th>\n",
       "      <td>4/30/2021 10:14:40 PM +00:00</td>\n",
       "      <td>5/1/2021 12:14:40 AM +02:00</td>\n",
       "      <td>00:15</td>\n",
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       "      <td>High</td>\n",
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       "      <th>1</th>\n",
       "      <td>4/30/2021 10:29:40 PM +00:00</td>\n",
       "      <td>5/1/2021 12:29:40 AM +02:00</td>\n",
       "      <td>00:30</td>\n",
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       "      <td>48</td>\n",
       "      <td>0</td>\n",
       "      <td>398</td>\n",
       "      <td>397</td>\n",
       "      <td>396</td>\n",
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       "      <td>High</td>\n",
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       "      <th>2</th>\n",
       "      <td>4/30/2021 10:44:40 PM +00:00</td>\n",
       "      <td>5/1/2021 12:44:40 AM +02:00</td>\n",
       "      <td>00:45</td>\n",
       "      <td>59</td>\n",
       "      <td>50</td>\n",
       "      <td>51</td>\n",
       "      <td>0</td>\n",
       "      <td>397</td>\n",
       "      <td>396</td>\n",
       "      <td>395</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>High</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4/30/2021 10:59:40 PM +00:00</td>\n",
       "      <td>5/1/2021 12:59:40 AM +02:00</td>\n",
       "      <td>01:00</td>\n",
       "      <td>58</td>\n",
       "      <td>51</td>\n",
       "      <td>50</td>\n",
       "      <td>0</td>\n",
       "      <td>397</td>\n",
       "      <td>396</td>\n",
       "      <td>395</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>High</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4/30/2021 11:14:40 PM +00:00</td>\n",
       "      <td>5/1/2021 1:14:40 AM +02:00</td>\n",
       "      <td>01:15</td>\n",
       "      <td>57</td>\n",
       "      <td>49</td>\n",
       "      <td>50</td>\n",
       "      <td>0</td>\n",
       "      <td>398</td>\n",
       "      <td>397</td>\n",
       "      <td>396</td>\n",
       "      <td>...</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>High</td>\n",
       "    </tr>\n",
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       "<p>5 rows × 107 columns</p>\n",
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      "text/plain": [
       "                           UTC                  UTCTimeZone   Time   \\\n",
       "0  4/30/2021 10:14:40 PM +00:00  5/1/2021 12:14:40 AM +02:00  00:15   \n",
       "1  4/30/2021 10:29:40 PM +00:00  5/1/2021 12:29:40 AM +02:00  00:30   \n",
       "2  4/30/2021 10:44:40 PM +00:00  5/1/2021 12:44:40 AM +02:00  00:45   \n",
       "3  4/30/2021 10:59:40 PM +00:00  5/1/2021 12:59:40 AM +02:00  01:00   \n",
       "4  4/30/2021 11:14:40 PM +00:00   5/1/2021 1:14:40 AM +02:00  01:15   \n",
       "\n",
       "   Avg IL1 (A)  Avg IL2 (A)  Avg IL3 (A)  Avg INe (A)  \\\n",
       "0           57           50           50            0   \n",
       "1           57           49           48            0   \n",
       "2           59           50           51            0   \n",
       "3           58           51           50            0   \n",
       "4           57           49           50            0   \n",
       "\n",
       "   Avg Phase-to-phase voltages U12 (V)  Avg Phase-to-phase voltages U23 (V)  \\\n",
       "0                                  402                                  401   \n",
       "1                                  398                                  397   \n",
       "2                                  397                                  396   \n",
       "3                                  397                                  396   \n",
       "4                                  398                                  397   \n",
       "\n",
       "   Avg Phase-to-phase voltages U31 (V)  ...  Avg OutV1 (V)  Max OutV1 (V)  \\\n",
       "0                                  399  ...              0              0   \n",
       "1                                  396  ...              0              0   \n",
       "2                                  395  ...              0              0   \n",
       "3                                  395  ...              0              0   \n",
       "4                                  396  ...              0              0   \n",
       "\n",
       "   Min OutV1 (V)  Avg OutV2 (V)  Max OutV2 (V)  Min OutV2 (V)  Avg OutV3 (V)  \\\n",
       "0              0              0              0              0              0   \n",
       "1              0              0              0              0              0   \n",
       "2              0              0              0              0              0   \n",
       "3              0              0              0              0              0   \n",
       "4              0              0              0              0              0   \n",
       "\n",
       "   Max OutV3 (V)  Min OutV3 (V)  Quality   \n",
       "0              0              0      High  \n",
       "1              0              0      High  \n",
       "2              0              0      High  \n",
       "3              0              0      High  \n",
       "4              0              0      High  \n",
       "\n",
       "[5 rows x 107 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_orig = pd.read_excel('Energy_Mgmt.xlsx',header=1)\n",
    "# for CSV files function will be pd.read_csv(Filename.csv)\n",
    "\n",
    "data = data_orig.dropna(subset=['Total active energy (kWh)'])\n",
    "data.to_excel('Energy_Mgmt_cleaned.xlsx',index=False,sheet_name='Sheet1')\n",
    "# for CSV files function will be data.to_csv(New_Filename.csv)\n",
    "\n",
    "print(data.shape)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Getting data\n",
    "I have used a dataset for forest fires with name forestfires.csv containg different variables related to forest fires. I have considered **'Avg Total apparent power (kVA)', 'Avg Total active power (kW)', 'Avg Total reactive power (kVAR)', 'Avg IL1 (A)', 'Avg IL2 (A)', 'Avg IL3 (A)'** as input variables and **'Total active energy (kWh)'** as output variable. You can add you own data, input column names and output column name as independent and dependent variable variables respectively, as this is going to be multivariate analysis. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Training data\n",
    "\n",
    "During the training process, the regression model learns the relationships between the input features and the target output from the training data. It adjusts its internal parameters to minimize the difference between its predictions and the actual target values. \n",
    "\n",
    "The train-test split is a fundamental step in machine learning where the dataset is divided into two separate subsets: the training set and the test set. This division is crucial for assessing the performance of a machine learning model accurately. We have considered the the ratio train:test = 20:80 for better training of data and testing it on a smaller data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get data\n",
    "X_list = ['Avg Total apparent power (kVA)','Avg Total active power (kW)','Avg Total reactive power (kVAR)',\n",
    "          'Avg IL1 (A)','Avg IL2 (A)','Avg IL3 (A)']\n",
    "X = data[X_list].values\n",
    "Y= data['Total active energy (kWh)'].values\n",
    "# print(X, Y)\n",
    "\n",
    "# Train data\n",
    "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Linear Regression \n",
    "\n",
    "Linear regression is a fundamental and widely used statistical method in machine learning and statistics for modeling the relationship between a dependent variable and one or more independent variables. It assumes a linear relationship between the variables, aiming to find the best-fitting linear equation that predicts the dependent variable based on the independent variables.\n",
    "\n",
    "In its simplest form, a linear regression equation for a single independent variable can be represented as: \n",
    "\n",
    "y = b<sub>0</sub> + b<sub>1</sub> x\n",
    "\n",
    "where y is the dependent variable (target), x is the independent variable (input feature), b<sub>1</sub> is the slope (coefficient) of the line, b<sub>0</sub> is the y-intercept.\n",
    "\n",
    "For multivariate analysis, equation becomes:\n",
    "\n",
    "y = b<sub>0</sub> + b<sub>1</sub> x<sub>1</sub> + b<sub>2</sub> x<sub>2</sub> + b<sub>3</sub> x<sub>3</sub> +...+ b<sub>n</sub> x<sub>n</sub>   ----- x<sub>i</sub> and b<sub>i</sub> -- i is a positive integer\n",
    "\n",
    "where y is the dependent variable (target), x<sub>i</sub> are the independent variables (input features), b<sub>i</sub> is the slope (coefficient) of the line, b<sub>0</sub> is the y-intercept."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a linear regression model\n",
    "reg = linear_model.LinearRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
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      ],
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Fit the data\n",
    "reg.fit(X_train, Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 8.49394686  8.2891498   8.33379865 ...  8.21004537 14.12505927\n",
      " 12.33746209]\n"
     ]
    }
   ],
   "source": [
    "# Predict the dependent variable using independent varables from test set\n",
    "reg_Y_pred = reg.predict(X_test)\n",
    "print(reg_Y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.15129935037990805"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Find the y-intercept\n",
    "reg.intercept_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.02641277, 0.0421666 , 0.01373539, 0.04464885, 0.04022726,\n",
       "       0.03887718])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Find the coefficients\n",
    "reg.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9822973463939889"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Find r-squared score\n",
    "reg.score(X_test,Y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Find the prediction error\n",
    "pred_err_reg = reg_Y_pred-Y_test\n",
    "# print(pred_err_reg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting Target variables vs Independent variables - given and predicted\n",
    "\n",
    "# plt.plot(X,Y,'.',color='grey')\n",
    "plt.plot(X_test,Y_test,'.',color='red')\n",
    "plt.plot(X_test,reg_Y_pred,'.',color='blue')\n",
    "# plt.plot(Y_test,reg_Y_pred,'.',color='black')\n",
    "plt.xlabel(\"Independent Variables\")\n",
    "plt.ylabel(\"Target variable\")\n",
    "plt.title(\"Linear Regression\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting Target variables - given, predicted and prediction error\n",
    "\n",
    "plt.plot(Y_test,color='green',label='Actual data')\n",
    "plt.plot(reg_Y_pred,'-',color='blue',label='Predicted data')\n",
    "plt.plot(pred_err_reg,'--',color='red',label='Prediction error')\n",
    "plt.xlabel(\"Observed data\")\n",
    "plt.ylabel(\"Predicted data\")\n",
    "plt.title(\"Prediction error\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Evaluation metrics\n",
    "\n",
    "Also called performance metrics, are essential tools for assessing the quality of a machine learning model's predictions. They help quantify how well a model is performing and provide insights into its strengths and weaknesses. \n",
    "\n",
    "The choice of which metric to use depends on the specific problem, the nature of the data, and the goals of the analysis. It's often a good practice to use a combination of metrics to get a comprehensive understanding of the model's performance. And that's what we will be doing here."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. R-squared (Coefficient of Determination):\n",
    "\n",
    "1) Represents the proportion of the variance in the dependent variable that's explained by the independent variables.\n",
    "\n",
    "2) Ranges from 0 to 1; higher values indicate a better fit.\n",
    "\n",
    "3) Can be interpreted as the model's goodness of fit.\n",
    "\n",
    "4) It's important to note that R-squared can be misleading in certain cases, especially when dealing with complex models."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9822973463939889\n"
     ]
    }
   ],
   "source": [
    "r2_reg = metrics.r2_score(Y_test,reg_Y_pred)\n",
    "print(r2_reg)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. Mean Squared Error (MSE):\n",
    "\n",
    "1) Measures the average squared difference between predicted values and actual target values.\n",
    "\n",
    "2) Penalizes larger errors more heavily.\n",
    "\n",
    "3) Lower values indicate better model performance.\n",
    "\n",
    "4) MSE = Σ(y_actual - y_predicted)^2 / n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.18265787369372508\n"
     ]
    }
   ],
   "source": [
    "mean2_reg = metrics.mean_squared_error(Y_test,reg_Y_pred)\n",
    "print(mean2_reg)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. Root Mean Squared Error (RMSE):\n",
    "\n",
    "1) Derived from MSE by taking the square root.\n",
    "\n",
    "2) RMSE is in the same unit as the target variable, making it more interpretable.\n",
    "\n",
    "3) RMSE = √(Σ(y_actual - y_predicted)^2 / n)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.42738492450450927\n"
     ]
    }
   ],
   "source": [
    "rt_mean2_reg = metrics.mean_squared_error(Y_test,reg_Y_pred, squared=False)\n",
    "print(rt_mean2_reg)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4. Mean Absolute Error (MAE):\n",
    "\n",
    "1) Measures the average absolute difference between predicted values and actual target values.\n",
    "\n",
    "2) Less sensitive to outliers compared to MSE.\n",
    "\n",
    "3) MAE = Σ|y_actual - y_predicted| / n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.34685142116787004\n"
     ]
    }
   ],
   "source": [
    "mean_abs_reg = metrics.mean_absolute_error(Y_test,reg_Y_pred)\n",
    "print(mean_abs_reg)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Applications:**\n",
    "\n",
    "Linear regression is used in various fields for prediction, understanding relationships, and making inferences. Common applications include economics (predicting sales based on advertising), medicine (predicting patient outcomes based on medical history), and social sciences (predicting voting behavior based on demographics). Linear regression forms the foundation for more advanced regression techniques and serves as an essential tool for understanding and modeling linear relationships in data.\n",
    "\n",
    "**Strengths:**\n",
    "\n",
    "1. Simple and interpretable.\n",
    "2. Provides insights into relationships between variables.\n",
    "3. Often serves as a baseline model for more complex methods.\n",
    "\n",
    "**Limitations:**\n",
    "\n",
    "1. Assumes linear relationships, which may not hold in all cases.\n",
    "2. Sensitive to outliers.\n",
    "3. May not perform well with high-dimensional data or complex interactions.\n",
    "\n",
    "Linear regression forms the foundation for more advanced regression techniques and serves as an essential tool for understanding and modeling linear relationships in data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Decision Tree Regression\n",
    "\n",
    "Decision Tree Regression is a powerful supervised machine learning algorithm used for both classification and regression tasks. In this context, we'll focus on Decision Tree Regression, which involves predicting a continuous target variable based on input features. Here's an overview of Decision Tree Regression:\n",
    "\n",
    "1. **Decision Tree:** A decision tree is a hierarchical structure that makes a sequence of decisions based on the input features, leading to a final prediction. It resembles an upside-down tree where each internal node represents a decision based on a specific feature, and each leaf node represents a prediction.\n",
    "\n",
    "2. **Splitting Criteria:** Decision trees make splits at each internal node based on specific criteria to maximize information gain or minimize impurity. Common splitting criteria include Mean Squared Error (MSE) for regression tasks.\n",
    "\n",
    "3. **Leaf Value:** The value assigned to a leaf node in a decision tree is typically the average (or median) of the target values of the training examples that reach that leaf.\n",
    "\n",
    "4. **Overfitting:** Decision trees are prone to overfitting, where they learn the training data's noise and perform poorly on new, unseen data. Techniques like pruning and setting minimum samples per leaf can help mitigate overfitting."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import tree "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a Decision tree regression model\n",
    "Dtree = tree.DecisionTreeRegressor(max_depth=6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Fit the data\n",
    "Dtree_Fit = Dtree.fit(X_train, Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 4500x1500 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Draw the decision tree\n",
    "fig, axes = plt.subplots(figsize = (15,5), dpi=300)\n",
    "tree.plot_tree(Dtree)\n",
    "plt.show()\n",
    "# fig.savefig('E_Mgmt_DTree_6.png') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 8.44467641  8.2599278   8.36075949 ...  8.2599278  14.22727273\n",
      " 12.15      ]\n"
     ]
    }
   ],
   "source": [
    "# Predict the dependent variable using independent varables from test set\n",
    "Dtree_Y_pred = Dtree.predict(X_test)\n",
    "print(Dtree_Y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Find the prediction error\n",
    "pred_err_Dtree = Dtree_Y_pred-Y_test\n",
    "# print(pred_err_Dtree)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting Target variables vs Independent variables - given and predicted\n",
    "\n",
    "# plt.plot(X,Y,'.',color='grey')\n",
    "# plt.rcParams['figure.figsize'] = (15,6)\n",
    "plt.plot(X_test,Y_test,'.',color='red')\n",
    "plt.plot(X_test,Dtree_Y_pred,'.',color='blue')\n",
    "# plt.plot(Y_test,Dtree_Y_pred,'.',color='black')\n",
    "plt.xlabel(\"Independent Variables\")\n",
    "plt.ylabel(\"Target variable\")\n",
    "plt.title(\"Decision Tree Regression\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting Target variables - given, predicted and prediction error\n",
    "\n",
    "plt.plot(Y_test,color='green',label='Actual data')\n",
    "plt.plot(Dtree_Y_pred,'-',color='blue',label='Predicted data')\n",
    "plt.plot(pred_err_Dtree,'--',color='red',label='Prediction error')\n",
    "plt.xlabel(\"Observed data\")\n",
    "plt.ylabel(\"Predicted data\")\n",
    "plt.title(\"Prediction error\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Evaluation metrics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. R-squared (Coefficient of Determination):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9814119987337151\n"
     ]
    }
   ],
   "source": [
    "r2_Dtree = metrics.r2_score(Y_test,Dtree_Y_pred)\n",
    "print(r2_Dtree)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. Mean Squared Error (MSE):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.1917929855647728\n"
     ]
    }
   ],
   "source": [
    "mean2_Dtree = metrics.mean_squared_error(Y_test,Dtree_Y_pred)\n",
    "print(mean2_Dtree)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. Root Mean Squared Error (RMSE):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.4379417604713814\n"
     ]
    }
   ],
   "source": [
    "rt_mean2_Dtree = metrics.mean_squared_error(Y_test,Dtree_Y_pred, squared=False)\n",
    "print(rt_mean2_Dtree)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4. Mean Absolute Error (MAE):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.34364391975734915\n"
     ]
    }
   ],
   "source": [
    "mean_abs_Dtree = metrics.mean_absolute_error(Y_test,Dtree_Y_pred)\n",
    "print(mean_abs_Dtree)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Importances of features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Avg IL3 (A)                        0.816584\n",
      "Avg Total active power (kW)        0.159406\n",
      "Avg Total apparent power (kVA)     0.021905\n",
      "Avg IL1 (A)                        0.000942\n",
      "Avg Total reactive power (kVAR)    0.000925\n",
      "Avg IL2 (A)                        0.000238\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "Dtree_imp =pd.Series(Dtree_Fit.feature_importances_, index=X_list).sort_values(ascending=False)\n",
    "print(Dtree_imp)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Applications**:\n",
    "\n",
    "Decision Tree Regression's applications span finance (stock price prediction, credit risk assessment), healthcare (patient recovery prediction, disease risk factors), marketing (customer segmentation, churn prediction), retail (demand forecasting, price optimization), and manufacturing (equipment failure prediction, quality control). Its versatility in modeling complex relationships makes it valuable across industries.\n",
    "\n",
    "**Strengths**:\n",
    "\n",
    "1. Decision trees are easy to understand and visualize, making them particularly useful for explaining model decisions. \n",
    "\n",
    "2. They handle both numerical and categorical data. \n",
    "\n",
    "3. Decision trees can capture non-linear relationships between features and the target variable.\n",
    "\n",
    "**Limitations:**\n",
    "\n",
    "1. Decision trees can be sensitive to small variations in the data, which can lead to different tree structures. \n",
    "\n",
    "2. They are prone to overfitting, especially when the tree is deep. \n",
    "\n",
    "3. Decision trees might not perform well when the relationships in the data are complex and involve multiple variables.\n",
    "\n",
    "Decision Tree Regression's ability to handle both numerical and categorical data makes it suitable for a wide range of applications where understanding and interpreting the relationships between variables are important. However, as with any machine learning technique, it's important to carefully preprocess data, select appropriate features, and fine-tune hyperparameters to achieve optimal results for a specific application."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Random Forest Regression\n",
    "\n",
    "Random Forest Regression is a powerful ensemble learning algorithm used for regression tasks. It builds multiple decision trees during training and combines their predictions to improve accuracy and reduce overfitting. Here's a brief overview:\n",
    "\n",
    "1. **Ensemble Method:** Random Forest combines the predictions of multiple decision trees, known as an ensemble, to provide more accurate and robust predictions.\n",
    "\n",
    "2. **Randomness:** It introduces randomness by selecting a random subset of features and a random subset of data for each tree, reducing the risk of overfitting.\n",
    "\n",
    "3. **Prediction Aggregation:** Predictions from individual trees are aggregated through averaging (for regression) to produce the final result."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a Random forest regression model\n",
    "RF = RandomForestRegressor(random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Fit the data\n",
    "RF_Fit = RF.fit(X_train, Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 8.15877795  8.44731147  8.39818556 ...  8.0281539  14.12833333\n",
      " 12.11      ]\n"
     ]
    }
   ],
   "source": [
    "# Predict the dependent variable using independent varables from test set\n",
    "RF_Y_pred = RF.predict(X_test)\n",
    "print(RF_Y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9783918807080592"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Find r-squared score\n",
    "RF.score(X_test,Y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Find the prediction error\n",
    "pred_err_RF = RF_Y_pred-Y_test\n",
    "# print(pred_err_RF)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting Target variables vs Independent variables - given and predicted\n",
    "\n",
    "# plt.plot(X,Y,'.',color='grey')\n",
    "# plt.rcParams['figure.figsize'] = (15,6)\n",
    "plt.plot(X_test,Y_test,'.',color='red')\n",
    "plt.plot(X_test,RF_Y_pred,'.',color='blue')\n",
    "plt.xlabel(\"Independent Variables\")\n",
    "plt.ylabel(\"Target variable\")\n",
    "plt.title(\"Random Forest Regression\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting Target variables - given, predicted and prediction error\n",
    "\n",
    "plt.plot(Y_test,color='green',label='Actual data')\n",
    "plt.plot(RF_Y_pred,'-',color='blue',label='Predicted data')\n",
    "plt.plot(pred_err_RF,'--',color='red',label='Prediction error')\n",
    "plt.xlabel(\"Observed data\")\n",
    "plt.ylabel(\"Predicted data\")\n",
    "plt.title(\"Prediction error\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Evaluation metrics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. R-squared (Coefficient of Determination):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9783918807080592\n"
     ]
    }
   ],
   "source": [
    "r2_RF = metrics.r2_score(Y_test,RF_Y_pred)\n",
    "print(r2_RF)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. Mean Squared Error (MSE):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.22295488643838535\n"
     ]
    }
   ],
   "source": [
    "mean2_RF = metrics.mean_squared_error(Y_test,RF_Y_pred)\n",
    "print(mean2_RF)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. Root Mean Squared Error (RMSE):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.4721809890692184\n"
     ]
    }
   ],
   "source": [
    "rt_mean2_RF = metrics.mean_squared_error(Y_test,RF_Y_pred, squared=False)\n",
    "print(rt_mean2_RF)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4. Mean Absolute Error (MAE):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.3587559964971667\n"
     ]
    }
   ],
   "source": [
    "mean_abs_RF = metrics.mean_absolute_error(Y_test,RF_Y_pred)\n",
    "print(mean_abs_RF)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Importances of features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Avg IL3 (A)                        0.340304\n",
      "Avg Total apparent power (kVA)     0.315603\n",
      "Avg Total active power (kW)        0.282738\n",
      "Avg IL2 (A)                        0.053413\n",
      "Avg IL1 (A)                        0.005266\n",
      "Avg Total reactive power (kVAR)    0.002675\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "RF_imp =pd.Series(RF_Fit.feature_importances_, index=X_list).sort_values(ascending=False)\n",
    "print(RF_imp)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Applications:**\n",
    "\n",
    "Similar to Decision Tree Regression, Random Forest Regression finds applications in finance, healthcare, marketing, and other fields for prediction tasks.\n",
    "\n",
    "**Strengths:**\n",
    "\n",
    "1. Improved prediction accuracy compared to single decision trees.\n",
    "\n",
    "2. Handles non-linear relationships and interactions well. \n",
    "\n",
    "3. Robust to outliers and noisy data. \n",
    "\n",
    "4. Provides feature importance rankings.\n",
    "\n",
    "**Limitations:**\n",
    "\n",
    "Complex Interpretation: Random Forest models can be difficult to interpret due to their ensemble nature, making it challenging to understand the precise decision process for individual predictions.\n",
    "\n",
    "1. Computational Demand: Training multiple decision trees and combining their predictions can be resource-intensive, especially for large datasets or numerous trees.\n",
    "\n",
    "2. Memory Consumption: Random Forest models can require significant memory, particularly with a high number of features or trees.\n",
    "\n",
    "3. Overfitting Risk: While Random Forest helps mitigate overfitting compared to single decision trees, it can still overfit noisy or small datasets.\n",
    "\n",
    "4. Limited Extrapolation: Predictions beyond the range of training data might lack reliability, potentially impacting performance in uncharted domains.\n",
    "\n",
    "Random Forest Regression is a versatile and powerful algorithm that can produce accurate predictions in various domains. It's a popular choice due to its ability to handle complex relationships and mitigate overfitting."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Ridge Regression\n",
    "\n",
    "Ridge Regression, a type of linear regression, is a regularization technique used to prevent overfitting and improve the stability of the regression model. Here's a concise overview:\n",
    "\n",
    "1. **Regularization:** Ridge Regression adds a regularization term to the linear regression cost function, penalizing large coefficients.\n",
    "\n",
    "2. **L2 Norm Penalty:** Ridge Regression uses the L2 norm (Euclidean norm) of coefficient values as the regularization term.\n",
    "\n",
    "3. **Tuning Parameter:** A hyperparameter called lambda (α) controls the strength of regularization. Higher α leads to more regularization."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a ridge regression model\n",
    "ridge = linear_model.Ridge(alpha=1,max_iter=1000,solver='auto')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Fit the data\n",
    "ridge_Fit = ridge.fit(X_train, Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Predict the dependent variable using independent varables from test set\n",
    "ridge_Y_pred = ridge.predict(X_test)\n",
    "# print(reg_Y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.15130268215653153"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Find the y-intercept\n",
    "ridge.intercept_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.02642447, 0.04215344, 0.01373535, 0.04464886, 0.04022751,\n",
       "       0.0388779 ])"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Find the coefficients\n",
    "ridge.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9822973177831474"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Find r-squared score\n",
    "ridge.score(X_test,Y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Find the prediction error\n",
    "pred_err_ridge = ridge_Y_pred-Y_test\n",
    "# print(pred_err_ridge)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting Target variables vs Independent variables - given and predicted\n",
    "\n",
    "# plt.plot(X,Y,'.',color='grey')\n",
    "plt.plot(X_test,Y_test,'.',color='red')\n",
    "plt.plot(X_test,ridge_Y_pred,'.',color='blue')\n",
    "# plt.plot(X_test,reg_Y_pred,'.',color='black')\n",
    "plt.xlabel(\"Independent Variables\")\n",
    "plt.ylabel(\"Target variable\")\n",
    "plt.title(\"Ridge Regression\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting Target variables - given, predicted and prediction error\n",
    "\n",
    "plt.plot(Y_test,color='green',label='Actual data')\n",
    "plt.plot(ridge_Y_pred,'-',color='blue',label='Predicted data')\n",
    "plt.plot(pred_err_ridge,'--',color='red',label='Prediction error')\n",
    "plt.xlabel(\"Observed data\")\n",
    "plt.ylabel(\"Predicted data\")\n",
    "plt.title(\"Prediction error\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Evaluation metrics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. R-squared (Coefficient of Determination):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9822973177831474\n"
     ]
    }
   ],
   "source": [
    "r2_ridge = metrics.r2_score(Y_test,ridge_Y_pred)\n",
    "print(r2_ridge)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. Mean Squared Error (MSE):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.18265816890344813\n"
     ]
    }
   ],
   "source": [
    "mean2_ridge = metrics.mean_squared_error(Y_test,ridge_Y_pred)\n",
    "print(mean2_ridge)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. Root Mean Squared Error (RMSE):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.4273852698718665\n"
     ]
    }
   ],
   "source": [
    "rt_mean2_ridge = metrics.mean_squared_error(Y_test,ridge_Y_pred, squared=False)\n",
    "print(rt_mean2_ridge)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4. Mean Absolute Error (MAE):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.34685199849869963\n"
     ]
    }
   ],
   "source": [
    "mean_abs_ridge = metrics.mean_absolute_error(Y_test,ridge_Y_pred)\n",
    "print(mean_abs_ridge)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Applications:**\n",
    "\n",
    "Used in scenarios where multicollinearity is present, such as economics and social sciences. Suitable when there's a risk of overfitting due to a large number of features.\n",
    "\n",
    "**Strengths:**\n",
    "\n",
    "1. Reduces overfitting by controlling coefficient magnitudes.\n",
    "\n",
    "2. Improves model stability and generalization to new data.\n",
    "\n",
    "3. Can handle multicollinearity (high correlation between predictors) effectively.\n",
    "\n",
    "**Limitations:**\n",
    "\n",
    "1. Bias-Variance Trade-off: Ridge Regression strikes a balance between bias and variance, but excessive regularization (high α) can lead to underfitting.\n",
    "\n",
    "2. Feature Selection: Ridge Regression doesn't explicitly eliminate features, making it less suitable for feature selection tasks.\n",
    "\n",
    "3. Scaling Sensitivity: Proper feature scaling is crucial for Ridge Regression's performance, as it's sensitive to feature magnitudes.\n",
    "\n",
    "4. Non-Linearity Limitation: Limited ability to capture complex non-linear relationships in data due to its linear nature.\n",
    "\n",
    "Ridge Regression provides a balance between fitting the data well and preventing overfitting, making it useful for cases where there's a concern about the model's complexity and potential instability."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Lasso Regression \n",
    "\n",
    "Lasso Regression, another form of linear regression, is a regularization technique that helps prevent overfitting and feature selection. Here's a brief summary:\n",
    "\n",
    "1. **Regularization:** Lasso Regression adds a regularization term to the linear regression cost function, penalizing large coefficients.\n",
    "\n",
    "2. **L1 Norm Penalty:** Lasso uses the L1 norm (sum of absolute values) of coefficient values as the regularization term.\n",
    "\n",
    "3. **Tuning Parameter:** The hyperparameter lambda (α) controls the strength of regularization. Higher α increases the penalty."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a lasso regression model\n",
    "lasso = linear_model.Lasso(alpha=1,max_iter=1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Fit the data\n",
    "lasso_Fit = lasso.fit(X_train, Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Predict the dependent variable using independent varables from test set\n",
    "lasso_Y_pred = lasso.predict(X_test)\n",
    "# print(lasso_Y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.39455750864570227"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Find the y-intercept\n",
    "lasso.intercept_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.        , 0.        , 0.        , 0.03269461, 0.05220888,\n",
       "       0.07775008])"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Find the coefficients\n",
    "lasso.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9809242721936722"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Find r-squared score\n",
    "lasso.score(X_test,Y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Find the prediction error\n",
    "pred_err_lasso = lasso_Y_pred-Y_test\n",
    "# print(pred_err_lasso)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting Target variables vs Independent variables - given and predicted\n",
    "\n",
    "# plt.plot(X,Y,'.',color='grey')\n",
    "plt.plot(X_test,Y_test,'.',color='red')\n",
    "plt.plot(X_test,lasso_Y_pred,'.',color='blue')\n",
    "# plt.plot(Y_test,lasso_Y_pred,'.',color='black')\n",
    "plt.xlabel(\"Independent Variables\")\n",
    "plt.ylabel(\"Target variable\")\n",
    "plt.title(\"Lasso Regression\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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TLU5EjNltg8FgKGSOlOmqRjEYDAZDlJT26apGMRgMBoNbSrc+yKfQFINSqpFS6m+l1Aql1HKl1P+87jWVUhOVUmu8/wMn1xsMhhKPxwOPPw5paYmWJH4cIXqhUFsMucCDItIC6ALcrZRqCTwGTBKR44BJ3nODwVDKmD4dXn4Zbrop0ZIUBqV7vmqhKQYRSRORBd7j/cAKoAFwEWBNdh8DXFxYMhQleZ48tuzbgkc8UYf1iIct+7aQ54nN6N6RyLIdyxgxb0RkjzGybvc6hs0aFlPYaauX8NKPX8dXoBJIXp6uX+8/eDgh6Y9dMpZZm2e58puTl8PTfz3NrsxdPPXXU2TmBLeUnP95mzGGgqOUagq0A2YDdUUkDbTyAI4KEeY2pdQ8pdS8nTt3FoWYUWM3u33hpRfy387/2JW5K+p4dh/aze0DbueDsR8A0ZvdHjFiBJ988kn0N1CCOXn4ydz5652FFv8ZY87g/t/vJyMrI+qw3bsl88TFV8RfqBLG8vSlAKxMX52Q9K8fez9dh5/pyu+YxWN4ftrz1Hm1Di9Me4Eh/wwJ618pPc3YE309sERQ6IpBKVUZ+A4YKCL73IYTkZEi0lFEOlp2fIobdrPbKSkpfPfJd7Y9Yd2b3bZaGdaakmjNbt9xxx3ccMMNsdxC1IgIHtvX4H8eilhNkCeKWBRCPjtPip8gJRiP6Geel6jS89WdMGKRK685edoygfX9ZuVmBfXnsS06eeYZSE6GrOBeSzSFqhiUUilopfCZiHzvdd6ulKrnvV4P2BEqfEmiU9dObErdxD9T/4na7Pa1/a5lzy6fuYpozW4PGjSI1157DdDmMLp06ULr1q255JJL8s1gnHHGGTz66KOccsopHH/88UybNi3ofbz66qv5sj777LMApKam0qJFC+666y7at2/PtGnTHOebNm3i4YcfplWrVpx88sl89dVXAAEmyA1HFklJ1gq+BPbH7z7OlTd/e2GRFv4qJbz7rj4+eDAmyYo1hWZET+mc/hBYISJv2C79BNwIvOz9/2NcEjzjjEC3K66Au+6CzEw477zA6zfdpH/p6XDZZc5rkye7Tjo3N5fJf06mwyk9OZhRPmqz2xVqLee09t3Ar9Lv1uz2pEmT8sPccMMNvP322/To0YNnnnmGwYMHM8xrZTA3N5c5c+bw22+/MXjwYP78809Hen/88Qdr1qxhzpw5iAgXXnghU6dOpXHjxqxatYqPPvqI9957j9TUVMf5d999x6JFi1i8eDHp6el06tSJ7t27A04T5IYjiyRvYVsiVnZ7kuGfh+CUdyAly9HytxPsXkrjpj2FaV31NOB6YKlSapHX7Qm0QvhaKdUf2AhcXogyFJjcXN1UrFw58Jrd7HabTu246JyHWLIkuNntL774ljJlYN++QLPb1as0pmOHszic6bSO6tbstkVGRgZ79+7Nt7t04403cvnlvuy99NJLAejQoQOpqakB4f/44w/++OOP/P0YDhw4wJo1a2jcuDFNmjShS5cu+X7t59OnT+fqq/W91K1blx49ejB37lyqVq3qyIuShHiSIKtqosUo0SRZ/RGJbDGEoX9/uOoqOPtsmDP+eJh4K2TWgrMfj2wTqXjeUtwoNMUgItMJnX3uRoSiIVwNv2LF8Ndr1w55feVKrRiC2TOym91evmY/hzLKAoFmt1999W0aNDiHSpXAMg1kN7udm5MMQF5usiP+cGa3s7O1raWToujOLleuHKAHzXNzcwOuiwiPP/44t99+u8M9NTXVcU/B7jEU/uHihlCoBU72L6/CrFvJfiwDyhdaMgWmVi044QSI906rTz8NnTvDBRfEHofPFlTxLEVHj9Y/EcjJ9haFG0+HQcLWN4dB7/Dhrde+NLYYjpiVzyISdjponicvaAEXaWDJ4/GEnaJ6zjnn8MEHw8nNzQEkqNntHdu3MX9+dGa3d+7UZq/32Ybzq1WrRo0aNfLHDz799NOgVlvt9+wv6+jRozlw4AAAW7ZscWw5Goru3bvz1VdfkZeXx86dO5k6dSqnnHJKxHAF4ueR8JzO98ycTA7nRT8l8nDe4ZDTEnMW6llFRTmwmJGVEbX1zt27YeZMd373Zu11He/zz0PfvtGF8SfZ22TI80hMzyfPk8f+7P0OtwJNCvCSk5fDwcPOgQGr24tNpwGw+p/gY2L2Libru9+XHVqmUPJu332QzKwc1zIXNUeMYli/Zz0Lty0MeX3htoWk7k2NOt4FaQtYun1pyOsDBgzg+BNO4Lrr2tP3wuBmt59+4n7at4/O7PaECd9zzTVtmfbPJEeYMWPG8PDDD9O6dWsWLVrEM888E1SuHE9OQH707t2ba665hq5du3LyySdz2WWXOfZdCMUll1xC69atadOmDb169WLIkCEcffTREcMViAW35h9WerESJw+PfnC77Yi2VHqxkFo0UbJy03aqV6jG5U/+Uijxz9g0gxqv1OCHlT9EFa7GKzWYucml5vHDKmsP5Ryiy6gu4T0HYcDPA6j6sq87b0HaAqq/Up0vl30ZkzwWZ396NpVfcvYNJyU7FbJ4QjQDvK2fvVl72Zelv42mA29kU8amAK9zt8yl+qPt+GpZ4JqWo2tVokG75bGIXzRYm4UU51+HDh3En3///TfALRxz162WucvSg17zeDwyd8tcmbtlbmC4ufoXMt65eTJ3YaYsW70vpN8dezJl7lyR+Usyg8a9bpMOu3T1Ptf3s3yNDrNmg/swjrRD3G9R8O+/IitXRv8M7eiGvPd4EMIgoo8jTDjK7RUQ2bBtr8O95knzBERe+nieK9nCpm/z9+n4fwVEKjSOLk/cpjVs5jBhEHLvb/dGFS+DkLdmvRWVTBajf17qi+e6s6MObz0fj8cjIiKj5o8SBiG3/HCLu/Ah8saK1379lmf/8cmKSPt+k4LGefvz07Sfkz/Nf0dA5NOJ8wP8PvTBjwIi3e/92LVs8QaYJzGUuaVia09XhJm2ZjUJVUwjSkmQWwGIXLMuVkhSwgYFrel9cRt+yGgIZQ5FH25TF9hfL05CFG/cbPEab5LsQ2Zj/4BPY4tHEBQq5EyhGCN1kOSXPSFbDBZKIC8l/3RfRmBRun1DdQC2rTgmFgkTyhHTlRQO64VLxMeTMHa0gm3tEi1FfBi6CV7bFn24D2fC199H9lcE2BdOFQbbU2voQdXlTQs1HTtJcfqexJs3u9IqwyBhx6rmcYjUKVtSst/lEI8jfz+GnS0ht2K++9vPNeMxP6tvyd44PXklr5gteRLbkDh9TFY8sbUYSih5ZRMsQJwLQinaxu/HQ49l8+YiTbJArJzdBIA1U9sHXFu/HiZMCB021gpTUpxKF6tF/+8sva/4qj9Oj0OsfoohWlnTnLv3rVxShVdecXqxFEOJWMfhR4lVDOXLl2fXrl1xUQ5HZIshoQi5ubsoX74YzwONwKrF1bjmmvjFF6l2ff31UBBzWMr7jgebOtq8OZx7boiAu4+J+RsrcIth7Tnw1Tfx7UKy8MuHZL++JE+IriSfEb3IZj7yWwxhuqVWrIgYTUIosWMMDRs2ZPPmzbg1sJeerv8HexC5nlzSdx8guYyHFXtWuA5nv55yMIucQ+WD+j2QeZhd6WVRZQ6zokzZgLCZh7PI3FeelINZJOe6Kyy37cji8KHyHMzOIuegM0xenp7GWKtW6JpQpPuKBo8HMjKgenV3c7p37IC1a8tz550NC5y2LrQSo9Czs8Nf93jEZxYiApHK3rFj9S9Wk1jKpRwBvLWOVceNg87RB3V77yEZq5sxeZ4sSCbOFk3DtxjiUcvPVwx+65PstGyp0zrjDL3g7vrrC55uXIhlxLqof8FmJUU/Oh98FsDk/yZLg9cbCoikHLXGdTj/6yee+2f+8etvHZTDuYfl3t/ulW37t8mon/TsjErNlgYNe9UjUwREWvaZ4vp+2l40WUDk4v9Nlv/2/CcP/f6Q5HnyRETk7vuyBEReeDlbRER2Htwp9/52r2TnZru6r9/X/i4j542UpyY9Jct3LJd5W+bJC1NfCCnLo0/o9J5/MVsO5RySu3+9W3Zn7s6//t2/38nYxWMD0p65Yp1c8MhX+bNO/Jm7Za68PO3l/PNX/3lVpm2YJv8b/7/8OLbt35Z//O3yb+XSD++U/3anSkZWhtz1y11y8PDBkHLb82DtrrXyyB+P5MsSaVYSiNQ4dnV+nouIZOdmyz2/3pN/PSc3T8JhT/+T38LPSrL8+j9ry/3+CfdLbl5uyLQue0i/L7T4RjZnbBGPxyOP/PGIDJ87POi7YJ+hc/LVX4a9j1B8/dcKRzzBWJi2UP5vyv+JiMhzk5+Tsz85W9bvXu+Q4UCWns13w9P6Ozn+rKmu0rfCbz+wXQZPHiyLty3W7oMQnirrkGvgqzMcsrY6f3LQOAcM9s5KUnkO//mzkxZ/Kj+s+EFERO4ZovO8UZfZIWWz0i+sWUqYWUmxccaYMyBX1+JzdsY+e2Bl+kqsBd1jJs6j+dl7eHvO26QdSKNP+WcLLmgYrvjmCuZuncs1J19Du3rtmLN1NtCdmZtnAj148I8H+WTxJ3Rp2IVrTo7c/3HO2HPyjz9Y8AHbD24H4InTnwjqf9p/s4AeTEudSc2F//Lu3HdJUkm8de5bAPT7uh8A17a+1hGu5/l7yFp/BTOv38iprRoHxNvpg04APNrtUQAenviw7eowAAZOuB/4HIDL3hkEw5dyePlPnHTuDN6b9x7H1DiGB099MOI9X/LVJSzdsZSb2t5Eizot8t3DdWPsObSbWZt3cmqjUwH4Zvk3vDP3HeBtQO9HUCZ0ZdGB2+6Sq769itlbZnNVq6voUN/Xzz101lD6Ht+Xns16Bg2XXz9ecRln3fkB3wztypAJY6HyNuCOsGku/eJKK4ujwk1XUoeRHfCIh6e6P8UzE1+E5MNc8e0VzL11br6f3LyCWWe989c7+X7F97w8/WUyn/QuaIw0+BwiLong4/pxusovz0p+nBFnOBVDSuwYQ9xYezZMfQoAlRyflYgikj9glusJND0R4N96yaJ4f+xN3RyPU25Bp+3xTqGwZJBo28dZVcnJjfxRWjNqRIQ8r6llNxsW5e7VW3Hk5sb+4az42afESO0BwJbVdfNXdbvdOMk/D91ij98/rVhmGqkICiKcnG6Vy86l7di5PRne2AJ/vhSVfNHgplvRkWcvHIKf3/d9M95+/DxrKlCM06uzsnT4/Lybdyt8/a3Dj3+3Vwz7bQVQpowVV8krZkuexPFm7B8w9WkAVJnQy/aj/cZ37yynp9Ytbe3Cd3wH13w2aqz/McSfUx5eziDz5xcip2cNbEaZRDzuetfaY30n2dUAqFDlEHu21IYhO9izrYo7WULlkb+7X+EUTvF4PPF9rlqc0HH6X8vM1OM+v/wS2Ie+e5e3OjvjkThLaBfIfUGeL/vCAeydf5Y+9iqGgrYY/v3yehicg2f2Xdrhl5GwxmkEKnCMIbjs+c801ODz6Cnw1TeAfbqqaTGUbMK0GCIWrh57r5xi9WJtCXX97+cxa3L1sEELVHyEG5DLr8kD6e7s0ueTo+doZy/q50aI6OJ2S1obmPk/3/mQHfB3mG65LK0YVLKw+LfOkFmHxX+d6CopySsDu6O0ArulM/syQn9ChbFBzf6Vp8DrmzmUGfnTXbNGTwp44onETMV2PctPIPOQ7z3ePqG/7wI+xRDr7KSD2+qDlMEz/rWQfvy7vSLWpUJ9dxu7wwptwt9SNqbFUMIpUIth/u0hL416I3D2zfz59rgLqcXgLQzWTDgH3lnN2iW13EeSX2Oy9aqGkNPuGs29RCw23l8Evw/znWfWgSmDQvv31n6Tk6PPzx3fPwZvrdddLDYCKv1+BcL6VRUIRV5MLYbwubLl+//B/gZM/7MaXluH+fgXnJZeSkoiIXsUu34VpjxD5Ur2osiqlev/1t7RMcvhomAOXOAWYrpqFKJYXUkeoxhKNuHGGGKtrSj7B2l72exmvKON+6mnYPFPuj9docje2RB+eyu/IPDvSkpfpbtbtm9y163ijcX7zxvJyr78PTm4nMH6xd3UUONijjmISElJ0T+rzFVdAdiX4XK02EtA4Z9ZI/+wMLqSLB6/8xjOOsvp5q+UrdOkpGK8RufPF2DyYD9H693zjjF4FUOsrR5x0ZXjv44h8hhD5GdrVVDM4HMxZH/2fnYf2h3gfijnEDsOOk1K5xJ6Yvq+rP2k7U/jcN5hNmZsjFmevVl7A0zx7jm0Rx+Eap1mbHR89C/4dfunjhwCc+5l1TJde82v6/vFlxvjACtZVeDLnzizV4jXxbZT175dFeCtVaxYlet64DdqBtlbMYEfnd1SZkEbYzsO+tbJbDuwLeCecnN9CWxYWQOG+N61YIPP85fuZ+6SfQ5z1vuy7Vuh6zALlh7gr3/Cm5iePdt5nnYgzXHuazFIyAqKP1v2bWH9nvUB7m57xUSETRmb2HFwB4dyXNgsnx58phtgG2OwdYl6CfcN+n/z9nfE/5u38B9jyMyJYHvLRQvMGtA2XUnFkIZDG1JriK8LxarF9fi4B3Vfq+v0LDBx3cSg8Rz1al3qv1GfRkMb0WRYEz5fGsP8PSXUeKUG1V+p7nCeNC/0Sz5twzSaDGvC2CVjw0Ws/1oVLb8Wg8UHrzVxL6u98Bj7u+tgi/4+FnYfz6QvWvHKdK+NgNTusDZw15M8FzO2YiE5WeK2eUqXUb6VXfVer8feQ87C2t5i2Ly2uvNakC6Qjq2rcEqbqtR4xdeyqPlK4M58HVpX5sxu1dwLuvhabvzhRrJzfZWbnQd2AbDlwCZXte2MrAwaXjmE5v/XI+DauIlpQUIE8tqM12g8rDF1X6tL/x8HuBQ+BAGDzzo/V/95Ok16/B3S/HajoY38vnlvMedJoW7VGkHD+LcYNk8JtUuP+xlS+Yqh6HvxCkypVwzO2piPuVvnBnVfsn1JUHerENuxPx1m3M8/6+cH9eePq+6SWffr/0G8WnsmhJI3GD7F4PdG7o1xi83NXV16FMQqKFdcyl+rZ+njj6cEVy4ZTYGCdnME6cZKir2lEiDLK3vC+s9z7HXkDOt28Nma4lsgxo2FzZ0cG+KkH9S15gz/jWRC1HZXpx6ACW9qo4R+7DzozsLA36l/5x9n58W6y5E4/ucrWHv2Lr6RycuXBQ2dseAsmH23Lza7ETtPSpAQkVdpL10KdepAxi6v4TwXLYbivoNdOEr/Arf04yDHZ9/ZI0JSqNrTnmPZtu4oODXYRW+YRTfBH28wt+bvcFG8hQ0kJ093/6QkBX+hw1GwikosL7NttOFAPVaNvQf6h/OvKcjge7CPLjm6YQJvRFbtLoIsfgWCs1XgN/gbxX259hrO32Hn5jOWYlJKUC6qgHlh9JPbe5HDFWDpVfDbO1B/nqswIbEGn0OM1cz58my4MsiFb5xrFDx5sbwQuqVSJlln3OuvazMyS6dbE0lcdCWV4Hp36VcM76x2nEb68FfPaQah7JUsuh526E2WLbtIkVAFnA1iLcpJSQ6uGIJWtq2aSpAKq1J628aIiNU9FXloPNQ9Zu+u4yKh+OO/G1dhkhtmxoyngLNpomZ7G8epVaAql2MM4T4Nt4rh309vhb/76JN154T3HBK/weeCDuLHWGM/nJOXrxgCiKrFEN5fcexqKv2KwY9ID8E+n/mgfVvYvBT4wWfeMuaHualz2PUS/lgthrLJwc1kBxPDN101OE895Tr5MLE4UvT5tnl3O220QMbWgiThSNftc/J+6NF2azlbDP6L38IEHPeR7zijQXzGRH4fyqFD+6lSTp9affNKSYHXMbidR5AVj8qAX6Gbv44hxm/OXYshMPLDuXlUJMWRtoTx74/yNtMidSXpymrx6m464hRDpJqP1eTevx+ee85xJab0Al6KD2dFFT7rcA7MvQO6hm6hHN7tG0RftQoO7fN2nSW4JuI/NzwU/o9k9GhtCtrpJ/jHI0HckpIl+paa7Tnl5ACHQ0zt9Xue9u4X/8I97BjD4pt8x0M3I2f9607OCOTZpmbmT/NUUuB1DIVi+joCKsmDAJ4QXVxuFYWbWUHBFOfhHF/CAWlFyM8vvsD3rpgWQwnCk+S3WlmT7P26GzaEffZx6yXXOfxFLnhieNoCv/8OnTtrUwYAP7/RFyZ04te8pTwXZKKEAvIO+mZanHgigDY+V7AXTjn+ucXRYnDZxbpgTjlOt/WC9PcblxARb8HkTpjURY1ZNLGVu8SBQ34zEy+7DJAgwo/9Ff5zLh7Is5lr8M/vqBZmFULhYFcMDqUVg5JwP15S8Jqvrf0J2Fo+McYX6zqC7JxwkwLCxzloENzyjDVuFd7vbbf5jn/8UX/7PQInhhUpJXd0JEbuf+cP7nzjV/jiR3g+cN2CUooR80Y4lQLAL+/7+wyZxq5Duxg2600Atu13N80PdE2/Tx84+wK9f/Ta9bksnaAL+QUTTmb8xOj2NS6QYgiy8jna9Dbu+4/6r9fPP7/s68uCzhIbeJvufnhl+it8vfzrwHiRkGsi0lIrB7iFUwpTUqcwcMJAh9t1AzIcheVPPwWGExFYe16Ae9gxBr8McWNQMTMnkw/mf5B/ftnXl/nWubjAMuB4y4+3sDJdj68p/wV/oqLuWvJ4hKzcLK789koGTR7E6R+dzvXjrg8Ys4vrOroIg8+Lti8IuubCH/HENvg8ae1k7vntHv7Z+A//7tAbl3jQyiJSxXD1Gg8KZ1fS0u1L6f9j/3wDjxajR/sy7eKL9d4MieaIazGMeDDw47aTnKS489c7iWSKOBxb922BWt5pg1HUzjIP6RdmwdIDQBVmrV4D+ExAfz9rHuee7dzWMNx3WLDmv9/K57CEGNhMP5G0NF+B/t2NI2n15TeEmqr02CRr09wrnLFLmFXEO052KxngNbMODOszLN9t4pxUkPCrwkO1WOy7c/kXiv4yL0hbAJwSPAErbFpHbnvvPuBWAL4bd5hWdd4Cng0rn13ODXs38NGij2DdRuBSVJK7AjusgT6E8WvG8/Wfq6DuN5BTiek7D/P+BYeomFLR5jGOdU1vXJZiCFbzvvu3uxl/7fjw0cS4wOyJSU+T5lnCu3Pfha2fAi1Iz9wJNIgcWJICKleXfn0pa3ev9ZqRPz4mmYqKI67FEAnlZl6fG8Qyxh57Fcr/Yy6bEihbuGI7Pi0GN9gb/7ZE01vC69t851k1WTLZZg3VJR6PFHxmSlhCF+4WoQrNtx4+LWSYqMxu272OnuE7/vInvn/hUvfxIGQdUnp1+EKtgAO6koi+Zi8eWLe8hrZfNe0J+Oo7GDWb/fv9WgzRRRs+zUy98C8nx6sY/N92l++oqzGGIF4O7C3nOzlkLZhzf4dWq8xSaNaUc2tCSXHmiGsxRCJJKThcMbLHcGTVgOVWrTeKTyVC7bxs2cC4wjZpY1VKhyvCocDVuG4okDIa+h80mhkYJxK73SFvsN27oWZN9L0dcK54ty+IC2l9O4bWl78yyzwYW6Vj6aTwrSI7HhF27vBWSpZdDWgl4K8Igt1npOmqu9O8BgP/9s13DsiXQphcM/bDavTuHOduKhccHP0tZNSHcx6AtXpTbN9KnShm63lbldaU81j3/ihKjGLwI0kpePFgZI/hWGrbqWzVRQWLy0a5IC2GcIW/IBw6BFn7qkaX0DsrYF/gjmp2FiyA44+HypV1SnEho2n+amg7IgUzYT19Opx+OowbB3z2G2zowVnLY47ONf7K7L/VoS2xxotghbtK8rjrSgrzHENvV1EYLQZnnPNnhZiR57Kb1k2LIdgQlmev9xuY8JY90ajSBt/dlKQWQ6F1JSmlRiuldiilltncBimltiilFnl/4Tv8E4DbOfVb/43RvEQBKF8uukE0EejZE7YvCV3jnDkTfv3VzzGCUjh4EDp0gMsv1+fW3P9JH57BnF/CzwaKpebt8UhMu6EBoGCu15rIlCnABj3dY9IkmxfbRx6yWyiGFou/zPEwr35oU4uw1yWInEkKPhrsNzYVZQkeavA/8LnEQTX4RVHn6NCD9u7yNJ7dkN7uoazIlS1LtAMb9HhCqWoxKKXqAI8CLYF81S0ivSIE/Rh4B/jEz32oiITeMSPBuJ3BsGNN+MIzGHmZEcxe+9X+/WeOlEsJIluY71Ak0AKnP6ee6vMblCA1I2sfgFlBlmRsW187fIIxdG95RGK3ye92rnvE6zEoBr/ZjkWxFsAjgYv0VJLgyXNTBwzT+pTgyiRQYca/v6d33wygcmCtftb9bG17GK4LFio6gj6ZMocgN/ZWnv83tfufi+GjmWRcNi3mOIsKN2/LZ8AKoBkwGEgFIlp0E5GpQKC962LOJ0Pauva7P3t/VHFnbW8SpTROypWLsoEXRTm0YWPwOdv+YxhpaXD00frYI3khzRhHS2ZOZshrBWkxpGfuyjcsF6rW72gxhCjYYmkxBHZ/FX4nuccjAVOCA8cXopcjVM180GDhkiv0s9u6fys5eaE3u1qZvtJVWv416jwJ3WLYNL0H63avCxufm1cn2JTgpIp7A9yiaWn5VwQ2j78GgPNanR7MuzOsCPO3zmf5jiLo8wyCm5Kmloh8COSIyBQRuQXoUoA071FKLfF2NdUI5UkpdZtSap5Sat7One4sOxY1vcacWaTplSsbnWKIpiw98/LI88EBpi/enH+8LyOZujfdG5VMofpmL/7y4rDBYh1jmLdqC4///gwA/+4MsbrYJtP2g9uDeolFL8Xc/VUAPCJccN8ffq5uS7Nw01WDM/q96vzwjZ6s0eCNBqRnpoeMo8W7LRxWWkNVrPyVy4a9XrP0QW5jb9Yejn3bN9PtrXEzAj25mEL7+ozXA9yO7hidlYKAZP0yLSnFvZn5EfNG0PGDjrQa3orUvakFkiMW3JQ0lvpOU0qdr5RqBwTuVemO4UBzoC2QBgQ+DS8iMlJEOopIxzp1EmOMLRLzRoTezjMe+NdOgg1/2LsNAmqGUaSV4bfPQCh2ZfrVrL79qsDmFgAmfhB6qWeBupLm3If8+TIAuw6FKrR8Gbf30N6QMkSLvy4Lu04gTjrE4xGYPdDh5v/axGLY0eOJzx4X9tbM7v2hW4l2kpT7sbXJU+PXf1+rUvUgrlE1GRwklXEv26JtiyBbm7Zxa/I8nrhRDM8rpaoBDwIPAaOAgbEkJiLbRSRPRDzAB4Rc7VNCWOjCpnQ0+H2wAX3Fwb5MWxj/wiXWafThrgQrIOOy0fy0J0NeKtDgswt0Qam8x/HrSvKXuShaEAVJI9J01QJvDzpIyDzoi2P/Ppct4EALdj52HQeLbvB5DeLJXY4EscMVbIZXNLORCqAY1s9oCy8dgC0dCm8nxDC4eTJ7RCRDRJaJSE8R6UCMYwdKqXq200uA4DttHKlEtMIYZXTxKIfivYl8TP3bhbt/8qEd9cnZEX6WmZsCN6IRvTBRxGuOflAx/Z5hbGMMgfHEQup6n/n4JJc3bT36vNwgxdWeY+GHMb7zYPfm5n5/9jd541zV7sN93vkrqWi6kjYv1Ob9SSu+iuFtl24OlFJfADOBE5RSm5VS/YEhSqmlSqklQE/g/qikPcKJNKvFzSKmglKYBXTINEVc7zkcjlD5kZXuq69k7C4X1I+b+/aP35MHK1ZAlouNzOLWleQiot3L20cdr6tpocMXwbo+Yb08eIdvcaHbqeFW0l++2s2132jYtImgU7QL+s4FtBiiMLdv/9QToRhCTldVSnVF72VWRyn1gO1SVSBip5+IXB3E+cOoJTTkU5gbfuxa2pHTT4dpEWbSFbQA++HN0GMJodOUAi1wi4aMXYGK4fBhAo0quuCtV2vw87fQpw+MH184itqfgqQRTqm4qhD4bRQUjLWrXLQYtnZynIrAwIGRk/f6DuIUXgFdGsLiSNCNrtyKQeCzCDBmGC6s7SguW79GSbgWQ1mgMlp5VLH99gGXFb5oBv/vZvfOlIACKlz/frSFxPTpkf0ELyAKt8S7/upy5OYWTUslWDdLz57QrGGlIL6d+D+vn7/V61YmTPDGXRTrGII9nzh0UxXG+Ijb7jMRePNNl36DpxQ2zMEQhg48QRWK+8zcv68ghiV8d+JvjbUoCKkYvFNTBwNdRGSw7feGiKwpQhmPODziCTp/+Zm7WlCt4WaHm30Kpv+HNm9LAffcDUIsH15B+X1CGXbsLCrFEOg2I8gMyGhpft/djFoQusE8Y82KgidCiAI8iJO2IOznLUwWCwXfBU6nIaxKXwW4Vwwfv1crop9eY3qR68ll7np3ayXsrNoVPMya9MBibs8q9/t8vPpAO6dDNBOaLKX0y0iysoq++9bNGEOmUupVpdRvSqm/rF+hS3YEkr8b2f4GDPxqaHBP+50zhZfvKNzxe/9ZGIkYYwDYE2IaaTQUtRE2O+vffpf5v4UuVD54PD5rYoI9Hwkyj3/7bwNchfVFUiCxHDw39TlvlC4j3XViRC9/p/7NtgPbSPsl0Fx+pMF2T6huyniaEC8A61ZGbq3GG7crn1cS5cpnQ/Qo24eybMytLgOFnq7KyovjIJT/jJZgngpfWRTVGEOhjvOtOyf0tezqcUkiaIsh2Oya5VcFOIVbK+KReLULk/LfoXCKKOpW2iAJ2Ikvn1itDBcTxRDzGp4CkIiVz4aiYnf8NwMJPre7KEw9FDwON93khToOkNGk8OK2CFIIBtsXOxhFtVJ79fjeEdM77bTo4927O84FucS281uYCGPyGm6XwMLCzeiIY+UzsJXYVz4bwuD/AbuazmcrCAqnfC76RVrBKKp0Y5njX5wIVgt3Z0Av9Baa4DWiF6dyN321NmGRqG5J1xSTFkNxVQz2lc9vo6ermvUHhYCz7HNZQP3hsypS3KdDFijdIprKnaj7ixfBFGj60nZBfAYJG0ExxAurAhPvrA7Z2ouk7EO1DOJYSZg0KfauuARMSoqsGETkF+9hBnpRmqGwsL/XbleZ5gVfkBUrCxc6z3N3Obs/ElXLK6oxhoLcXyIHty0KIv/6NSkhr8VVMbsYY4iFkPkfqea/64TYwkXBQw/BTpcKGpwt15y84rXA7W3CKHURua9QJDqSKWANJR4FU/voF8WyZFbk6YQFZfH2JegtQQqXwrI1VFSsTF9FrD29/7s5wh4jYd6vmZtmAl1dpZM67TTufWIr5Tt+ge6IiA+hNxOKsWANMkAfK4sWAYRWvOHIS0CLIZxKnAfMR2/O0x5Y4/21BRIgaunHv1xxa0umKAlWcGascb8ncawMmT6k0NOAEtDvHYEBPwVOQ40HQvj38dTRp0YV3zsv1ee1GfHdr2vUglFxjS+x2Ba4FacxBhEZA6CUugnoKSI53vMRgL/Bd0M8KODzL4qVtQmrFU9/rEiSCTcAG4lioccLafC8UJ77sivjGt3aPWuDX8gsnmb7w2GfiHJS7dZFnr6bTrT6aFMYFpW9boY4Y38ZikUhE4SEKYZ/ryiSZBIx0BdfiumLE4zfh8U5wpLd2gtFGeI7juguzci8DCxUSv3tPe8BDCo0iY5k/K1zRlkKF8mspKIfBytS8kr6Df7zcKFEG7GLbd1ZhZJuNBTVBIWiJhFTqN3MSvpIKTUe6Ox1ekxEthWuWEcmJWEOfVF0VyUSV+alizPzA01CxIVI7+anEwsn3SgoTXrB/homYvDZlfk/ryL4sZBliTu57vfFKJZEOxBaFIVaSS83I1GaCpd4IsRna8/CJG1Gr0SLUCgE3zCocCkeS/sKiV27Ei1BlNgKXUX0hXBRTKhJ1MrnoqK0dkccCexdWfSDtEVB587F01ZSiSXXU7KaDPbHv3X/FrJzD0cV/obvb4jsqYCU5C74RdsWRfRj9EJwSkK+lKo6i+1eatYs+uTDLXALK46IxLTvc1Gy59AeoARNVfPrx91xcGeCBAmNp5SPMRRkumpppiQUuiVARNfY7yU5uXgNPs9Hy6eAxsAe73F1YCPaDLchjvi/2NFvjFL4L1BJbjG4KToSMdBXEojXRj2FiSSgL74oSMRC13A7uDUTkWOA34G+IlJbRGoBFwDfF5WARxR+tpIy90e5NeAHZpuMghLrOoaSUKMuCB6PuLfflSBKwqw+19juxZWV5TjjZoyhk4j8Zp2IyHj0WgZDnPF/sZ+95fQESRKa0l4A5hZAMRT3WTsFoST0sJXWdzMRLQY3VdJ0pdRTwFh0nfY6oKTN9ykh2F6AYlr7+WZE/Df/KTJmD4zopSCDrKW1YIIS0oVYSvO/uLYYrkaP4I7z/up43Yo9Je5Dtcu78tKEiRGOdctrJFqEQiVWxVDSje9FwuNJTAEVDaWpK8ledhXLFoN39tH/lFKVReRAEcgUN0qaYihh4pZKYh189oiwbs9aIIY9KUsAJeFbOpCdmWgRCoVi2WJQSp2qlPoX+Nd73kYp9V6hSxYHStzUwxImbmkk1haDCPy+bkJ8hSlGeDzFf1ZSce1+jQ1fYZCIsSs3XUlDgXPwjiuIyGKge2EKFS9KXPO+VL3YJZOYWwwegc1d4itMMcIjFPtZSSXKsmwEpND3cg+Pq5XPIrLJz6lEzPZOxAYXBUFK0YtdUvHkxfYMBIHZ/4uzNMUHEYp/xSWOW3EWJxKhGNzMStqklDoVEKVUWeA+YEXhihUfTFeSIVpibWWWuNZplHg8kLqmUqLFCE9GhK1JDa5xo2LvAO4GGgCb0Vt73lWIMsWNkmYQbf+6wt8i0xCeggw+l2YOZpRj1MsnJlqM8OxrlGgJ4kZudtFvzmPHjWI4QUSuFZG6InKUiFwHtIgUSCk1Wim1Qym1zOZWUyk1USm1xvu/UOc+ljC9YCgG5MVoVqG0txgOHYxtI3tDbGye3Smh6btRDG+7dPPnY6CPn9tjwCQROQ6Y5D0vNEpai8GQeGJdyFXaWwwlYoGbIW6Es67aFTgVqKOUesB2qSqQHCliEZmqlGrq53wRcIb3eAwwGXjUvbjRYfSCIVrmb1kI9Iw6XE5O6VYMf6z9Ezgl0WIYiohwg89lgcpeP1Vs7vuAy2JMr66IpAGISJpS6qhQHpVStwG3ATRuHNugkmkxGKJGItZ5gpJ9uHQrBtb1TrQEhiIkpGIQkSnAFKXUxyKyoQhlstIfCYwE6NixY0xfXYmblWRIPCtiM0WSnV3K37W0jomWwFCEuBljGKWUqm6dKKVqKKV+jzG97Uqpet546gE7YozHFSVtHYOhGBDjzJbMrBKxtMdgcIUbxVBbRPZaJyKyBwjZBRSBn4Abvcc3Aj/GGI8rSvuAoKH48MarZtaOofTgRjF4lFL5nfxKqSa4WIqllPoCmAmcoJTarJTqD7wMnK2UWgOc7T0vNEyLwVBUfDK6QqJFMBjihpuVz08C05VSU7zn3fEOCodDREKZ5j7TpWwFxrQYDAaDIXrcmN2eoJRqD3RBW6m6X0TSC12yOPD3+inAsYkWw2AwGEoUIbuSlFInev+3BxoDW4EtQGOvW7Hnn00zEi2CwWAwlDjCtRgeBG4FXg9yTYBehSJRHEnGDAgaDAZDtIRbx3Cr93/0y0CLCXKoeqJFMBgMhhJHOJMYYVf6iMj38Rcnvqz5MuIYeYG44KIsfvmxfKGmYTAUGJUX84puQxFRdSPsKz5mw8NNV+3r/fUHPgSu9f5GAdcVvmgFp1zV/YUa/8uvHi7U+MNyyluJS7soqbaBvxetS7QUJZtaqxItQXTUmxeXaKocvzAu8YTlzjiZyk/KjU88cSKkYhCRm0XkZvR4QksR6Sci/YCTiky6AtL8zL+iC9DiO7j+bNfea1SL0xhGl6Fw/p3Rhen1FJTd53RLyoldhhN+iM6/CvMin/1Q7HIEpJNHSpnSuTNXXDjNxVKgLsOc50l+FZr6cyJG8cC3r7mXqaD0PzW2cGc57XHWOHFRwWWJRFX/zS1jpJhtm+rmi2tqGb7zsh04vpDkiStNu82BgS6aZ1YNpctQaP4ntPjWVfw1qpYNffGeEwLdLr02uN9zHoBOI8IktFb/v6ulz638fniimtNf24+Dh+/yRnD3Ky/xHZ/8BaQchLajodO7oWUpt1f/f6Jy8OuDFJwWbL6CH8nZ4a9f7F0g3/MZyqb4vaZPVoDTn3e61VoZOc14YC98rrw4urCR7hmg24v6uYR7Bnbqz4Oaq8P76fgBPFYVHqwH9zWHR+pAmzH6Wu8HoPkfwcNd2B+ASk2XU732IXfy+NP1NXikpu/8zAiW9iukQ5koKjhVN8KdreCxatBtiOPSMRePDfR/0c2R4zz3Xvhfs9DXu/8fNJ7Ksy/uc9b0g31nXYbCQyEMRRwz0XbiVQzdXvI5RVtZiyNuFMNkpdTvSqmblFI3Ar8CfxeyXHGhWfWmUHl74IWjlvqOK22Hk77RxxV3cWzNY+HSED1lfe7Te992fA+a/k2F8n79tu1H+o5r2z7Wylv1/6aTdW2txyD9oXqpWdH74dx7nC9MGduHePdJ8EQlOGqFVjihWjX+NUOAZ5Khue0FbPEtnHM/dHgfmk3yuadkwpOV4eL+cP49gfG0+hxafg0P1tcfeko2dA6Snj/l9wCgythqqZW2wWVXBfd/9QVwyXXQ9hOtZNp8RrmytnxOOQgpWXDm0z63qhvh3hbQvyvc67du5djxwdOptM15flN3uNtv/6kKu5znjafCabbCp94C6N/FqSCqhbE3+UADuO4cqLkm+PWbusNZT0KLH4I/A3/qLoKTvoPbOur7frwKPJ2i863je9pP+T00rd5UVySqbIOa66H8PqjoXYrkv0/yA/WhWioAlWrrZ1e3+Q4aVG0QWo7Wn/jJtdh33PthSLG9y6e/ortf7mgNV10E592tlZOFpaTuaB06PXsl4LaOVGjgvSc7FXZxXO3mwcOXCaLkGtqmtldMhxqpcLStK8peEWn1JdzSg4H3eyDJZiOrjt+Ox3efCH0egMo74Sm/HdlO/F4roJu6Q/8uHHW2V4md/iJcfhn0uxquvBSedrMGOf64WeB2j1LqEvSKZ4CRIjKucMWKD4+c9gjpmbsY5T1vMbQj/VvdAxV30+XobLo178hFty7lvGuPZctdH9PppFfp0aQHHy/6mL9qvsDAXtczcXo6L2w6C8rv5c8b/wRg1YSz2J+txy8mrf+LpduXMnt6Jbp3V9zZWaf11WVfceUgfXzZvQv4/tWjmHHfT7x3+q30Pb4vS7YP5P+8vQBL7ljCnC1zqFy2Mr2tLZDubM0tJ9/Fiv/28vKAiWzYu4HpG6fTs1lPxq8dz8UnfE/18tWZ0Pxzhlx/DdxwJhMffJNhp11AxT2d+OaeZwGYfPNfDBo1m8neaJ96ZzHt63WnVsValM2dSVevDH1PPJemnZpxa/tbyc7LptMgZ17e9PxEOtbrSEryMOZtnUfruq0ZedRIls4eCEDVJus48ZRNXNjzeZpUb8L13vADPhpCm2o9+GTwmcydDW//MJ3lyZ9RNe02hnzli79MuWxys8vx8SOXM37teI6t+SRb9m/h5rY3UzbZW3ilHOT1Ke8j8hoZ2Rm8cOLPeFb25aOvd+I56kNa1G5B3cp1GZC6jdnTKvHOxJ+pVakaF3kbWp0efIG5rz8JRy2Ba8+HoboboN+zX9LprPM5cPgAk5rcwI7vH6NK9WwWrdoF/50FQO1u3zP4tZ2klBnJba/uhMw6fHX1J0xL/449WXv4bO5EqL2Sdtd/zeFfhtC3/zJefq4yHK4Eqy8EYOApD9PphEb8eemrzHx3ACsnOfc3uKZXe249/TlWpq+kYdWG9PV7Bv4c01y46+zX6NeyHx8u+JAGVRsweMpgWh3VirOHHuS9//uZ8y46yP+umMDUDVNpdVQrsnKz+G/vfzw1sxxpQId6nXjonuo8/+gyzrzrJ05s8jTDpilWz4VFz47h217/MKBvJ2pW6UF/oGK1g1SrmkzTU+cx8+9qsONkmpz/NRuW3OATrHoqbG8DwItnvkirOq258AV96YluTzBj8wyqlK3C8bWOo2n1pvz7/UUM9+qDG3p249x+XzBtwzTeszWi+90/lTn/VKRicjV6P/glb1+mM+fMk1vz7RXf8sH8Dzi10al08+ZZ6w4HebPPm3zgl2dXnXgjExftZdeGCqByOXXAOA7sS+am/21g0sfJ/DqyM6OuHELzTrfzT+NcnrpFh2vVYR/LpunjsZd8TvLRK6hevjoL7ppFe++9NW1QiVQroUGKDvU6MO7KjSzbsYxZm2eRdOZzdKl9DuMXLKVmk600qfY4FVMqsjJ9Jfd1vo9/nv+NrNwxNKvejNS9qYxauI8B7QaEfwkKCxGJ+AOaAGd5jysCVdyEi9evQ4cOUhBA5LzzChB+EMIgJDcv11VaEHgcym9ystNtxgwR7msmDAoTMIR8djwekVyvuPv3i7RtK7JwoTNcRoZPRo/HL86TxwqXXiOc+opQZZMcPHww5D0Eu0d/9127RMaP952/OmaJgEj1lnNk7drw97cubbuOL+WAw73uC8cIt7WTLfu2BITJywuUZd06/f/o4zcKj1cSEClTa0PIdJv3niAgUrfRPkf+1Ln9GqHpX7IuPdWXht8z+HHlj9rtmSSh3B4BkYWrdjrif/ppn2wgsn6n8z4s9wULRL5Z+r3Q8ymh8VRJLpMnIHLhhWGzLSxXvPWSgMiTX44NuLZ7t8ivvwaGSU3V1yzKNVoqIPL5hNXS59k3tbzn3i0nvtZZ+vQR6dXLeS+tWweXZcQIfb1vX5GcHFuYW04Vug+WnTsDw4R67zIyRKZOFTlwwOYv5YBMnqyP//1X5JMJWu5ytTc7wubmOt9REZEWZ80UEHnu51FCm48FdD5Y7MrcJTxdRri8n7w7+z3f8xyE/Lo6SCYWMcA8iaHMjdiVpJS6FfgWeN/r1AD4oTCUVGGxfTt8H4fJtclJkaf8Va9uO7mvOdzcLaTf2bMhNdXp1rUrUPO/WMRzoBQke8WtXBkWLoS2bZ1+Urxj53fcof076HcdtP4cej8KDzaiXHLBNievWRP62DZ6LVdRdy1Vqr+R5iFa/D459Wuq/PIlqdwhqB985kmS/c2+oh9ceQnNmsHrr8PFz4yFcgfh3HuofVvoCXYtrh0Ft5zG8N//dORPyvGT4aZelE0J/T4kK++1JA+UzwACd3l76in46SfgWG3FvmpFvzy+6Ga4/HLatUN3WfR4Hm7pzstf6S7AjgXYIqHusVthkOKoprsCrtWoAeedFximSRN9zaJWz88AaNg4l/rtFusurM7vklRhP+PHwyRbT+X8+fB3iA7oAQNg7FgYNw7K2PswGs+AXs9Su3aQQHe3gAGdA5yrVoXTT4dKlbwOj9aAh46mRw9dZLdoAbVr6+eQVNHZ/ZSc7HxHAc5+4At4JonqdTLhgjvgltNo0sR3PUklQXIunPQdSUkKTn0VjvsVAEVs+4cXB9x0YN2N3tNvNoCIrAm381px5KgilHbDBjhsdafXXK9/ITglwTslVqgA6elQrVpkv26UooP7msPepuitvQNp0X4PXHkxx52dBfQLG1Xlislw5cWUP2Yx4FMO4h2wi/gBttS1AqXggQfg0Yl7YS3Q+V2ofHTIYJ4ymdB4BinJztlnHu8GyPmFfxCUXZNceh1MHsTR9Vs6/JQtC337ArMvhkM1KZvsN4De7uP8w1yPb5Cz4fG7WLpUF3KxkqS05vQUYDPnWl1+ZesxL1Ot5mLyxNfXLkGMV7YPY0QnORmuDTEvIyR1XE42qLA3wKlBQ4Hz7qJJt9XAn5HjSPLeT0qWVlb2S8pXA1Eo6P2IO7mKOW4UQ7aIHLZedKVUGVyY3T5SqVo1sp/iRK1aBQs/fTocHaxsjaAU8zx50OJHUspGnh6cnJQMLX5EpVR0uFsFkApo7rgnJy/0DBjrWtlk5+wzqzC1Fwr+OK41mQ43nkVS8ubgnlOyIGVrQDp28jy+gjfXk0urMGOzboiHYrAqC3mePKdiKObFQ5mkMnDKcFTVAmhWLw7FUID3sLjhRjFMUUo9AVRQSp0N3AX8XLhiGUoKp50WWzirIHHTEglXABeUHE9oxXA4Tzf9UpKCtxhcKwYv9sIzGP4tEzv2FoP9OFbiohi8LaY8yXMormAthuJEmSRd7MUzH6Fkdx354+aLexTYCSwFbgd+A54qTKEMpR+rIAnXHWMR6oOLtWZqD2cV/sGwrvnX5C3Zo1UMkQrhcPHZC7FwrRy3HPEtBsJXCiysewl1T3YlWJpaDGEVg1IqCVgqIh+IyOUicpn3uHg/+TgzqMcgzmh6RtThBp8xmO5Nukf26MfxtY5nxPlhFrz50bpua4adMyzqdMLRvl57Xu/9Oi+d+RJdG3aNOvxLZ75E5waBg4MWpzY6larlqvLE6U9EjKtCSgUaV2vMyAtGOtzfOfcd6laqS60K4fvDbmxzI7e0vSX/vH+7/lQoo3dc+6Cv/4RGH8/1fI4a5WvQuq6z32b4+cOpX6U+1cr7Bmcua3kZ93TyrT3oWL8jVctV5YY2ehrnUZWOol7lekHTGX7+cFrUDuzWuPKkK7mzo14Rf+5x5+a7n3dckJHhKLml3S2UL1Oey1teHnMcL/Z6kZoVatKyTkse7PpgfsvqtbPjs0p6YOeBXHjChUGv3d/lfi464aKIcVxy4iWO5wLQoEoD6lWuxxu9Qyz8tHF7h9upmFKRi0+8mNZ1W/Nmnzcd18uX8dlKO++48/i/nv+HQlGlbBU6Nwz9/hd3VKQyXin1GfC4iGwsGpEC6dixo8ybFx/7KQaDwXCkoJSaLyJRz19zM8ZQD1iulJoDHLQcRSS4KjcYDAZDicaNYhhc6FIYDAaDodgQbj+G8sAd6E2TlwIfikjxsg1rMBgMhrgTbvB5DNARrRTOJfgWnwaDwWAoZYTrSmopIicDKKU+BCIbbTcYDAZDiSdciyF/kq/pQjIYDIYjh3AthjZKKcvKlEKvfN7nPRYRKWHGHwwGg8HghpCKQcTsHm4wGAxHImYzXYPBYDA4MIrBYDAYDA4SsqGoUioV2A/kAbmxLNk2GAwGQ+GQmJ2mNT1FJD2B6RsMBoMhCKYryWAwGAwOEqUYBPhDKTVfKXVbMA9KqduUUvOUUvN27txZxOIZDAbDkUuiFMNpItIebWrjbqVUwKYFIjJSRDqKSMc6deoUvYQGg8FwhJIQxSAiW73/dwDjgFMSIYfBYDAYAilyxaCUqqSUqmIdA72BZUUth8FgMBiCk4hZSXWBcd79UcsAn4vIhATIYTAYDIYgFLliEJH1QJuiTtdgMBgM7jDTVQ0Gg8HgwCgGg8FgMDgwisFgMBgMDoxiMBgMBoMDoxgMBoPB4MAoBoPBYDA4MIrBYDAYDA6MYjAYDAaDA6MYDAaDweDAKAaDwWAwODCKwWAwGAwOjGIwGAwGgwOjGAwGg8HgwCgGg8FgMDgwisFgMBgMDoxiMBgMBoMDoxgMBoPB4MAoBoPBYDA4MIrBYDAYDA6MYjAYDAaDA6MYDAaDweDAKAaDwWAoLDZvhsOHEy1F1BjF4IaNG2H+/NjCpqfD88+DxxNfmQyx4/HATz+BSHzjzcyEgwfjG6ehZDBuHKxdq49FYPduyMiARo3gxhsTK1sMGMXghmbNoGPH2MLeeSc8/TRMnuw+zLvvwrJl7v2PGAELFwa6ezyQne0+nnjy+eeglP44grFiBcydG1vcmzfD669HX7BnZcH+/fDLL3DRRTB7trtwS5boDz0c6elQqRJUrqzPReDVV2HXruhkDIZI8OdbmvjwQ+jQIbow2dnw2muxv0fRcs898PPPge6LF8Oll8JJJ+nz99+HWrXg1FP1+XffuYtfBN5+u1hULo4MxbBxI8yaFXv4gtT2MzOd/wE2bdIFTah477sPRo8OHefKlc7m6Z13Qvv2gf4eeQTKl4ecnOjlLii//67/2+/b4vbboWVLOOWUwGtZWZE/9CuugIceguHDoVMnyM11Xv/00+CF/nvvQdWqsH27Pq9aNdDPX3/BhAk+PwBt2kC3bvr4wgt9x6A/ZhGoU0efK6X/z5ih8/+yy8LfS2amVjz79oX2M368fr6tW+v3Ih4tnT//hEcfjT18Tg5cfz38+6/PzeOB226LrlJjsWCB/oEuGF95Rd/3N98E+hWBvDz9DTz8MEyZEts9uEFEv4+5ubrCZr3XoPNg3Tpo21afHz4Mt94KW7fqc+uZNmzoLq3x4/W3b1UuEomIFPtfhw4dpEAcc4z+fLOz3fnPyhLxePTxgw/qsMccE+jvn39E1q93uv36q/a/cqU+HzJEnw8d6vPTsKF2O/lkfZ6bq9O0qFxZ5P77A9PbtUtk+3YdtnlzkQ0bdFirePLnssu0e06Ozy0zU2TRovD3f/CgyIwZOv5g/PyzyJo1ge4bN4q89pr+/+yzOu28PKcfj8cn74knBsZx55362rp1oeW74w7tp1Mn/X/LFuf1cuVE+vbVxxs2iNx0k8hTT4nUqaP9P/ec/v/ggyJr1zrD+op6kW++cbqJ6GdWubLP/+WXO8MkJ2v3uXN9blOm6OcUjP/7P59MIiJXXy1y3HH6eMYMkQULRO65x5nGDz/o69nZgfnrFiuu6dNjC794sQ7fqpXPbd067XbhhU6/AwaInHZa+PieflqH9XhEnn/eeb+hZLd+p5wS6CcjQ7+HdtLSRKpUEbnvPqd7VpbIRReJjB4dGM+YMTqNM87Q/486ynftllsCZbH/TjzR+R6JiHz7rcjChfq5rVghsnu379rQoaHvOUaAeRJDmZvwQt/Nr8CKoUsXfauHD4f2s3SpfrHT0rTft97S7vYH/dRT+qHOnu17YayHmJ2tCyGr8Dn3XJFbbxWpWTPwYR99tNPt4YdFypTRxzk5wV/2KVO0+1VX+cK+9ZYuxK3z1audYR59VKRsWaeb5Xfv3uD5YFc0Dz8sMmyYzr9gcVSqJPLvvz73f/7R7hMmiNx7rzPv5s/Xfvbv97mdf74z3o8/9l2bPVsrPxDZuVPkttv08SefiNx+uz4eMUL/f/31QBmuvlqfX3FF4AfboYPvuFEj7c+qCNj9XX65SNeuvmP/+Ox55V+QLVjgdPvyy+D5bSnQp58WmTrVGUeoAmfUKF3YgMj11wePV0TkwAH9XtkLpq1bRebN88V1993Bw27dGlqZiejvxcq/xYv1d2E9l/ffd/p1U9hZ30Rursgrr+jjPn1EWrTQz8ZeEfHPjxNO0O5r1ojMmqWPK1TwXW/fXrtZzxJ0hWDjRpElSwKfnZ0fftDubdoE+qldO/QzChWn5XbggP5/0kn6/8svO8PMmCHy228i+/aFz7cIGMUQihUr9G3Wry/yxhsi332nz3fs8M9B/bNqcEqJHHus+wd/3XXh/Zx4oshPPwW6Hzrka5WkpzsLTjv+Lw7oFsHhw063338XeeYZ/eJfeaV2+/VXXUOx19bt979li8jw4SK9eolcemlw+Rcv9vm3K7ZRo3zuVsHWs6dIx47B82nzZqfbf//pQvPRR3XN0n4f1nGwfIPAPP/yS99xkya+1lqkX79++r/1blg/e2Fw/vkiP/7ovJ6eHvp9qFEj0D0tTV/LzvbVZgcP1tdat3b6tc7LlQuMZ/x433GfPvpZ5uWJ3HWXdlu4UMe9d68+t7dWk5IC42vcWNfS587VrckdO7T7I48E/6aGDw+fn4MH6/f4gw9E2rXzuZ9wgq7wjBql87JzZy33zp0+P6tWBcZ30UW+4zffDP5t2b9hEV0h8n8mTZo43dq3d543aaK/pwMH9Pfz7LMiX32lr519ts/frFn6ezjvPHfvF/ha6aCV6dq17sMWgBKlGIA+wCpgLfBYJP8FUgz+mVy/vv5/zTX6/5gxgS9MNL/jj9ctg1jDW81N0C/zjBnO67/8IrJpk++j9/9VqyZywQXBr1Wq5Dt+5hmRmTN951YhZdWw3fyaNg10u+suXVDt2KG7QCK94H37Ot1uvjm4308/9R3/+ac7+V58MfbnEMtv2zZft5Tbn8cjcsMN+nj4cJGzzgrvv1evyHHecouvq8P63Xabr1AOpqTc/Kzu05o19Xv69NP6fXQT1r87yGpJ+//27fN9iyDy3nvRy9mokY6nYUORunWDf/cbN7qL68wznefWO61UoN9mzaKTU0Q/kwsuCN6SDRcu5uKvhCgGIBlYBxwDlAUWAy3DhYlZMXz/fWAmlykT/YtXUn/2pjPobgP7hyISv7SCKY1gL3hh3q+9hl8YP6tr60j53XFH/J6Z1WXi/wtVqSnI73//K/q8cnMfo0bFFncBKEmKoSvwu+38ceDxcGFiVgyvvVb0L0hJ+t1/f+HFfcopib8/80vsz23rIt6/UK2TkvorALEqhkRMV20AbLKdb/a6OVBK3aaUmqeUmrdz587YUsrKii3ckcLQoe793nyz8zzS3Ow5c0Jf69XLfbrxokcP937btSs8OY4kRo1KTLqxlhfFia5dE5p8IhSDCuImAQ4iI0Wko4h0rGPNEY8Woxhio1kzaNXK6XbggO94wgS9oGfQoNjitxYy9e4dW/hYiLRAzU79+tC8efzSrlEDzj/fd/7CC9HHYS2WKkn88EOiJQhk3LiiSaeBra770kvRh585M36yxEAiFMNmoJHtvCGwtVBScqMYwi0kKw4cdVT0YVJSog9zyy36f9myOrz/IiX7QqM+ffRCrnXrwsf58svB3f/v/2DYML16uahYutS9319/jXxvbhk8WCvaOXN8z+XJJ4P73bEjdDy3364XzcWCtSI3GsaMiS2twuSLLwoex9NPFzyOe++N7OfFF33HxxxT8DSLmlj6nwryA8oA64Fm+AafTwoXJuYxhiFD9GIk0LNndKeb7/fjj4Hzzd3+XnhBpGJF9/4HDnSeW7OjIv2mTYtOrvPO0wvQor2f1FSdX+eeG97f9ddHjqtRI/0/2NRC8E2V3bpVn59+emzPwM2At/1XvrzvfYjmV6uWnmppnffu7Tu++mrf8cSJznANGzrfuXCD49bU0FDXrcWG9mm8bn7Vqulw2dnBr/tPTz72WP1u7toV6PeEE2L/XuLx+/pr33HVqvq5BPNnn0EUbkbWY4+JDBrkLu0JE0Teftu3EDWYn3Xr9BTcQYOc33tqasHuO9x6kghQUgaftaycB6xGz056MpL/Ai9wGz/et+rZ/yOw5in7/y68MPSDsi+Usy/Ksn7du4v07+9bEzF0qH649o/TzbS8deuCz3d+9VXf8fPP+wZ6P/xQy3ToUGCYYOss7PO4rYU04eSpV09k0qTg16zC/cknfW7vvqvzwt9vaqpOy1rkc999zusDB4o88IDzWSQnB051BV3YV6vmO//4Y+fiJv/fvHl6YdLvv+sPePNm32KxUL+UFF1w2+Vv1Ehk+XLnIjdrwZ39FylP7fcs4lskBnpW3fvv62NrdfemTfq8Vy+9+CrSAO/xx+v3btQorZgqVdILI3v1EnnpJZFTT3WuObHWqNgXTvrfT0ZGoHuo2X5Tp+oFa/feq/O+bt3w8gZbZ5GcrP/b17n07x86Dmvacu/eTmVin1beuLGeOpyZqc8jVTL8sVdmRo/W72Zqqshnn+n1LatX+64HmypbpUr49DZt8q1xOXQo5qKvRCmGaH8FVgzOnHL+/Ncg9O+vP07rhXnoocAwdpMSHo9e1WuZLViwwHctJ0cvhrGbbLj3Xu3X/uLceGNgGkrpxT920wrWb+5cvXCvWjVt0uDXX/UHbq1N8L/Pq6/2KYYvv9QLj0CbirD8BFv56/+zFKJ9he7u3frFXbJEtxA++cR3bfz4wJrn44/7ZMzL02sB9u0TeecdnwkPa+VsVpZe/HTLLSINGmi3YC0WS+7y5fXxmjWR78GOx6M/QutdsFo81q9+ff3MrWcV6p3ybxVOnKivDx/uVMzffquV0hNPOE1qiGhFs2aNyMiROn+sRXubN+vr+/ZpOSdP9qVvyevfUrz2Wl0A2mv5zZs7Zb/zTi3LI49oBdirly4kRQIrPbVr+8JZysQqhFev9q3iti/WC5VXf/0V/DnaW8j16un/Vo379991ZQMClfA99+h8W79e1+xBL55cv14f9+vnTB90XlsLRKtW1f579nT6qVlTLyL0Z/p0fd3KKxGfWY9Jk/T5M8/o83DvI4h06+ZTAqDfdxG9xqhfv9CmaVxgFINbXnnF2bxMT9cfUPnyeoFZMNsz/g/S3zaPRTRNvrw8/YF//rmuOYuIjB3rS+Oxx/RLa5nosBfEbvjgAx3/zTfrwiQzUysUEb3U3vqoDx50Fpb2rhH/n6U8RHQtNFg+fPSRz7/VZXTRRb4VvJddFlrmHTu0Ys7MdLpbtTM7Vhpjxujz7dt96dlNPljh9u7V7uGwCqB77vGtGrff8xtv+D56O9nZWgnanx/4zDNYfPONLhD9adlSr/wORk5OZHtGlrKcMkUXbpUq6cJs+3a9ktgqxMqX14sFgzFsmPbjb/vr4EH9Dk6dqhdIWjz9tF6ol56uVwH7fzdffBHc7Mpbb+nC12ot+j8nEd1KTkvTNe2vv3aG93h0S9padZ6SoisS//3n82PZHLK+x19/9bWIre5LKz2PR7eqrPfIWoVtWSMIptzs2O/bWqH9/ff6PDdXZNkynYa1WrxzZ12hW79eK/fPPvOFt0xsWFYGrNbg7NnhZQiDUQzRYjWZ3fDGG9rvnXeGt7cUD5YvF5kzx+m2ebNWXi+/HL90fv89tCKzPojt27UxwI8+ErnkEnfxWi2hKVMC4wMdV7QsWRIYbtYsn+G5YEyc6Ou2iIZly3S+5OU5DRu6ITtbLyK0atP+z7GwsLqbrFaFP5ZdoxtuCB2Hx1N08trT/PJL3RVjFabR8N57+t6i5aGH9PhgODIz9XqIl15yH6/V4vz55+DxffihrhhYdtj8sRTLTz/pc8uky/Ll7mXwwyiGaNm3L9D6YjhWrCh8pVBc6NlT19LjhZuaV2GQlqZNjBQ127drJW5vbRQmGRm6xmy3ouvPzz/7WqZHOvfdJ1K9emR/0T6/ffv0otpYLd5a3dZWy2zTJt0dFWt8ErtiUDps8aZjx44yb968RItx5HDWWXqq7/Tp8Ylv505tP//oo+MTn8FQEKw9M4pb2ZeTA1Onwplnxi1KpdR8EYl6l7EycZPAUHqYNCm+8cW6QNFgKAzWroXU1ERLEUhKSlyVQkEwisFgMBxZNG8e35XtpZAjY2tPQ3Tcey9Ur55oKQwGQ4IwisEQSF4elDGNSYPhSMV8/YZA3nsv0RIYDIYEYloMBoPBYHBgWgyGQPr1g1WrEi2FwWBIEKbFYAgkKUmPMxgMhiMS02IwBGLfe8FgMBxxGMVgCOTvv2HTpsj+DAZDqcQoBkMgZ5yRaAkMBkMCMWMMBoPBYHBgFIPBYDAYHBjFYDAYDAYHRjEYDAaDwYFRDAaDwWBwYBSDwWAwGBwYxWAwGAwGB0YxGAwGg8FBidjzWSm1E9gQY/DaQHocxSkKjMxFg5G5aDAyFz6h5G0iIlHvrVsiFENBUErNi2Uz7ERiZC4ajMxFg5G58Im3vKYryWAwGAwOjGIwGAwGg4MjQTGMTLQAMWBkLhqMzEWDkbnwiau8pX6MwWAwGAzRcSS0GAwGg8EQBUYxGAwGg8FBqVYMSqk+SqlVSqm1SqnHEi0PgFKqkVLqb6XUCqXUcqXU/7zug5RSW5RSi7y/82xhHvfewyql1DkJkjtVKbXUK9s8r1tNpdREpdQa7/8axUVmpdQJtrxcpJTap5QaWNzyWSk1Wim1Qym1zOYWdb4qpTp4n89apdRbSilVxDK/qpRaqZRaopQap5Sq7nVvqpQ6ZMvvEcVI5qjfhWIg81c2eVOVUou87vHNZxEplT8gGVgHHAOUBRYDLYuBXPWA9t7jKsBqoCUwCHgoiP+WXtnLAc2895ScALlTgdp+bkOAx7zHjwGvFCeZ/d6FbUCT4pbPQHegPbCsIPkKzAG6AgoYD5xbxDL3Bsp4j1+xydzU7s8vnkTLHPW7kGiZ/a6/DjxTGPlcmlsMpwBrRWS9iBwGvgQuSrBMiEiaiCzwHu8HVgANwgS5CPhSRLJF5D9gLfreigMXAWO8x2OAi23uxUnmM4F1IhJu9XxCZBaRqcDuILK4zlelVD2gqojMFF0SfGILUyQyi8gfIpLrPZ0FNAwXR3GQOQzFNp8tvLX+K4AvwsURq8ylWTE0AOw72m8mfAFc5CilmgLtgNlep3u8TfHRtu6D4nIfAvyhlJqvlLrN61ZXRNJAKzzgKK97cZHZ4iqcH1BxzmeIPl8beI/93RPFLeiaqUUzpdRCpdQUpdTpXrfiInM070JxkRngdGC7iKyxucUtn0uzYgjWj1Zs5uYqpSoD3wEDRWQfMBxoDrQF0tDNRCg+93GaiLQHzgXuVkp1D+O3uMiMUqoscCHwjdepuOdzOELJWGxkV0o9CeQCn3md0oDGItIOeAD4XClVleIhc7TvQnGQ2eJqnJWduOZzaVYMm4FGtvOGwNYEyeJAKZWCVgqficj3ACKyXUTyRMQDfICvG6NY3IeIbPX+3wGMQ8u33dtUtZqsO7zei4XMXs4FFojIdij++ewl2nzdjLPrJiGyK6VuBC4ArvV2W+DtjtnlPZ6P7q8/nmIgcwzvQsJlBlBKlQEuBb6y3OKdz6VZMcwFjlNKNfPWGq8CfkqwTFbf4IfAChF5w+Zez+btEsCaifATcJVSqpxSqhlwHHowqchQSlVSSlWxjtEDjcu8st3o9XYj8GNxkdmGo2ZVnPPZRlT56u1u2q+U6uJ9v26whSkSlFJ9gEeBC0Uk0+ZeRymV7D0+xivz+mIic1TvQnGQ2ctZwEoRye8iins+F9aIenH4AeehZ/2sA55MtDxembqhm3JLgEXe33nAp8BSr/tPQD1bmCe997CKQpwFEUbmY9CzNBYDy628BGoBk4A13v81i4vMXhkqAruAaja3YpXPaKWVBuSga3f9Y8lXoCO6YFsHvIPXqkERyrwW3S9vvdMjvH77ed+ZxcACoG8xkjnqdyHRMnvdPwbu8PMb13w2JjEMBoPB4KA0dyUZDAaDIQaMYjAYDAaDA6MYDAaDweDAKAaDwWAwODCKwWAwGAwOjGIwlDiUUg2VUj8qbX10nVLqTe9aFZRSNyml3km0jP4opQ648DNIKfVQBD8XK6Vaxk8ygyEQoxgMJQrvIp3vgR9E5Dj06s7KwAuFmGaZwoo7Bi5GW/80GAoNoxgMJY1eQJaIfAQgInnA/cAtSqmKXj+NlFITvLb0n4X81du/KqUWK6WWKaWu9Lp38Bodm6+U+t1mimKyUupFpdQU4Emlbd8nea9VVEptUkqlKKWae9Oar5SappQ60eunmVJqplJqrlLq/0LdjFLqSa+cfwIn2Nxv9YZdrJT6zpvmqWi7T68qbXO/eTB/8c1uw5GIUQyGksZJwHy7g2gjhBuBY71OpwDXoo2jXa6U6gj0AbaKSBsRaQVM8Nqsehu4TEQ6AKNxtjyqi0gPERmMXlHaw+veF/hdRHLQm7Df6w3/EPCe18+bwHAR6YTeCyIApVQHtKmWdmjbN51sl78XkU4i0gZtmr2/iMxAr9B9WETaisi6YP5c5KHBEJbi1EQ2GNygCG4d0u4+UbwGxZRS36PNkPwGvKaUegX4RUSmKaVaAa2AibqHimS0CQKLr/yOrwT+Rhfm7yltIfdU4Bvl2xSrnPf/aWgzBaBNL7wSRObTgXHitS2klLLb8mqllHoeqI7uKvs9WGZE4c9gcI1RDIaSxnJ8BS4ASpsXboS2BdOBQMUhIrLaW0M/D3hJKfUH2krschHpGiKtg7bjn7zhanrT+AuoBOwVkbYhwruxNxPKz8fAxSKyWCl1E3BGAf0ZDK4xXUmGksYkoKJS6gYAr0XJ14GPxWfV82yl902ugB6s/UcpVR/IFJGxwGvoLRNXAXWUUl29caUopU4KlqiIHEBbW30T3eLI83Zh/aeUutwbXiml2niD/INuWYDu1grGVOASpVQFpa3X9rVdqwKkebu77OH3e69F8mcwxIxRDIYShWirj5egxw7WoK3nZgFP2LxNR3ffLAK+E5F5wMnAHKU3T38SeF70lq+XAa8opRZ7/Z8aJvmvgOtwdjFdC/T3hl+Ob/vY/6E3NJoLVAtxLwu8cS1C788xzXb5afTOfhOBlTb3L4GHld6pq3kYfwZDzBjrqgaDwWBwYFoMBoPBYHBgFIPBYDAYHBjFYDAYDAYHRjEYDAaDwYFRDAaDwWBwYBSDwWAwGBwYxWAwGAwGB/8Poh88Fq7QdCUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting Target variables - given, predicted and prediction error\n",
    "\n",
    "plt.plot(Y_test,color='green',label='Actual data')\n",
    "plt.plot(lasso_Y_pred,'-',color='blue',label='Predicted data')\n",
    "plt.plot(pred_err_lasso,'--',color='red',label='Prediction error')\n",
    "plt.xlabel(\"Observed data\")\n",
    "plt.ylabel(\"Predicted data\")\n",
    "plt.title(\"Prediction error\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Evaluation metrics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. R-squared (Coefficient of Determination):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9809242721936722\n"
     ]
    }
   ],
   "source": [
    "r2_lasso = metrics.r2_score(Y_test,lasso_Y_pred)\n",
    "print(r2_lasso)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. Mean Squared Error (MSE):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.19682540018073733\n"
     ]
    }
   ],
   "source": [
    "mean2_lasso = metrics.mean_squared_error(Y_test,lasso_Y_pred)\n",
    "print(mean2_lasso)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. Root Mean Squared Error (RMSE):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.44365008754731167\n"
     ]
    }
   ],
   "source": [
    "rt_mean2_lasso = metrics.mean_squared_error(Y_test,lasso_Y_pred, squared=False)\n",
    "print(rt_mean2_lasso)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4. Mean Absolute Error (MAE):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.3610127744085787\n"
     ]
    }
   ],
   "source": [
    "mean_abs_lasso = metrics.mean_absolute_error(Y_test,lasso_Y_pred)\n",
    "print(mean_abs_lasso)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Applications:**\n",
    "\n",
    "Used when feature selection is important, such as in genetics, image processing, and financial modeling. Suitable for cases where there's a concern about overfitting or too many irrelevant features.\n",
    "\n",
    "**Strengths:**\n",
    "\n",
    "1. Performs automatic feature selection by driving some coefficients to exactly zero, effectively excluding less relevant features.\n",
    "\n",
    "2. Effective for datasets with many features or multicollinearity.\n",
    "\n",
    "3. Enhances model interpretability by reducing the number of non-zero coefficients.\n",
    "\n",
    "**Limitations:**\n",
    "\n",
    "1. Sparse Solutions: While Lasso performs automatic feature selection, it might lead to excessively sparse models, excluding some useful variables.\n",
    "\n",
    "2. Scaling Sensitivity: Feature scaling is crucial for Lasso's performance, similar to Ridge Regression, as it's sensitive to feature magnitudes.\n",
    "\n",
    "3. Non-Convergence: Lasso's optimization process can struggle with highly correlated features or very large datasets, leading to non-convergence.\n",
    "\n",
    "4. Limited Variables: Lasso's feature selection might omit relevant variables if their impact is subtle or intertwined with other features.\n",
    "\n",
    "Lasso Regression helps create simpler and more interpretable models by selecting relevant features while reducing overfitting, making it valuable for scenarios with a high number of features and multicollinearity."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Support Vector Regression (SVR)\n",
    "\n",
    "Support Vector Regression (SVR) is a powerful regression technique that uses Support Vector Machines (SVMs) to predict continuous outcomes. Here's a concise overview, along with its limitations:\n",
    "\n",
    "1. **Hyperplane Fitting:** SVR aims to find a hyperplane that best fits the data, considering a specified margin around the hyperplane.\n",
    "\n",
    "2. **Kernel Trick:** SVR can handle non-linear relationships by mapping the data into a higher-dimensional space using kernel functions.\n",
    "\n",
    "3. **Epsilon-Sensitive Loss Function:** SVR introduces an epsilon-insensitive loss function that allows for a specified tolerance around the predicted values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.svm import SVR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Fit the data\n",
    "svr_Fit = SVR().fit(X_train, Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Predict the dependent variable using independent varables from test set\n",
    "svr_Y_pred = svr_Fit.predict(X_test)\n",
    "# print(svr_Y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([15.65785632])"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Find the y-intercept\n",
    "svr_Fit.intercept_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Find r-squared score\n",
    "svr_Fit.score(X_test,Y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Find the prediction \n",
    "pred_err_svr = svr_Y_pred-Y_test\n",
    "# print(pred_err_svr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Plotting Target variables vs Independent variables - given and predicted\n",
    "\n",
    "# plt.plot(X,Y,'.',color='grey')\n",
    "plt.plot(X_test,Y_test,'.',color='red')\n",
    "plt.plot(X_test,svr_Y_pred,'.',color='blue')\n",
    "plt.xlabel(\"Independent Variables\")\n",
    "plt.ylabel(\"Target variable\")\n",
    "plt.title(\"SVR Regression\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Plotting Target variables - given, predicted and prediction error\n",
    "\n",
    "plt.plot(Y_test,color='green',label='Actual data')\n",
    "plt.plot(svr_Y_pred,'-',color='blue',label='Predicted data')\n",
    "plt.plot(pred_err_svr,'--',color='red',label='Prediction error')\n",
    "plt.xlabel(\"Observed data\")\n",
    "plt.ylabel(\"Predicted data\")\n",
    "plt.title(\"Prediction error\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Evaluation metrics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. R-squared (Coefficient of Determination):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "r2_svr = metrics.r2_score(Y_test,svr_Y_pred)\n",
    "print(r2_svr)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. Mean Squared Error (MSE):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "mean2_svr = metrics.mean_squared_error(Y_test,svr_Y_pred)\n",
    "print(mean2_svr)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. Root Mean Squared Error (RMSE):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "rt_mean2_svr = metrics.mean_squared_error(Y_test,svr_Y_pred, squared=False)\n",
    "print(rt_mean2_svr)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4. Mean Absolute Error (MAE):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "mean_abs_svr = metrics.mean_absolute_error(Y_test,svr_Y_pred)\n",
    "print(mean_abs_svr)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Applications:**\n",
    "\n",
    "Used in finance for stock price prediction, in healthcare for disease progression modeling, and in various fields for time series forecasting.\n",
    "\n",
    "**Strengths:**\n",
    "\n",
    "1. Effective for capturing complex relationships, even in high-dimensional spaces.\n",
    "\n",
    "2. Handles non-linear data through kernel functions.\n",
    "\n",
    "3. Provides control over model complexity through tuning hyperparameters.\n",
    "\n",
    "**Limitations**\n",
    "\n",
    "1. Sensitive to Hyperparameters: SVR performance heavily depends on selecting appropriate kernel and regularization parameters, which can be challenging.\n",
    "\n",
    "2. Computational Complexity: Training SVR models, especially with non-linear kernels and large datasets, can be computationally intensive.\n",
    "\n",
    "3. Memory Consumption: SVR models, especially those with complex kernels, can consume significant memory resources.\n",
    "\n",
    "4. Interpretability: SVR models are less interpretable than linear regression due to their non-linear nature and reliance on kernel functions.\n",
    "\n",
    "5. Limited for Noisy Data: SVR might struggle with noisy data or outliers, affecting model performance and robustness.\n",
    "\n",
    "Support Vector Regression offers a robust approach for regression tasks, particularly when dealing with non-linear data. However, careful parameter tuning and consideration of its limitations are crucial for achieving optimal results."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Best Regression Model based on Evaluation Metrics\n",
    "\n",
    "So far we have calculated the evaluation metrics values. Now we will decide the optimum model based on these values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "Evaluation_metrics = ['R2 score','MSE','Root MSE','MAE']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "All_r2 = [r2_reg, r2_Dtree, r2_RF, r2_ridge, r2_lasso, r2_svr]\n",
    "All_MSE = [mean2_reg, mean2_Dtree, mean2_RF, mean2_ridge, mean2_lasso, mean2_svr]\n",
    "All_rt_MSE = [rt_mean2_reg, rt_mean2_Dtree, rt_mean2_RF, rt_mean2_ridge, rt_mean2_lasso, rt_mean2_svr]\n",
    "All_MAE = [mean_abs_reg, mean_abs_Dtree, mean_abs_RF, mean_abs_ridge, mean_abs_lasso, mean_abs_svr]\n",
    "\n",
    "r2_best = max(All_r2)\n",
    "MSE_best = min(All_MSE)\n",
    "rt_MSE_best = min(All_rt_MSE)\n",
    "MAE_best = min(All_MAE)\n",
    "\n",
    "Values = [r2_best, MSE_best, rt_MSE_best, MAE_best]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "Name_Reg_models = ['Linear Regression', 'Decision Tree Regression', 'Random Forest Regression', 'Ridge Regression', 'Lasso Regression', 'SVR (Support Vector Regression)']\n",
    "\n",
    "model_r2_best = Name_Reg_models[All_r2.index(r2_best)]\n",
    "model_MSE_best = Name_Reg_models[All_MSE.index(MSE_best)]\n",
    "model_rt_MSE_best = Name_Reg_models[All_rt_MSE.index(rt_MSE_best)]\n",
    "model_MAE_best = Name_Reg_models[All_MAE.index(MAE_best)]\n",
    "\n",
    "Regression_models = [model_r2_best, model_MSE_best, model_rt_MSE_best, model_MAE_best]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "Best_models = pd.DataFrame(list(zip(Evaluation_metrics,Regression_models,Values)), columns=['Evaluation Metrics', 'Best Regression model', 'Value'])\n",
    "print(Best_models)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Optimum Regression model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "most_recurring = Best_models['Best Regression model'].value_counts()\n",
    "print(f'The Optimum Regression model is: {most_recurring}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature Selection methods\n",
    "\n",
    "Feature selection is a critical step in machine learning that involves selecting a subset of relevant features from the original set of features to improve model performance, reduce overfitting, and enhance interpretability. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Feature Importance\n",
    "\n",
    "A heatmap is a graphical representation of data where values are represented by colors. While heatmaps are not typically used to directly visualize feature importance, they can be employed to visually explore correlations between features and the target variable, which indirectly provides insights into feature importance. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_np = pd.DataFrame(data.iloc[:, [45,33,57,3,4,5]]) \n",
    "X_df = pd.DataFrame(X_np)\n",
    "Y_np = pd.DataFrame(data.iloc[:,94])\n",
    "Y_df = pd.DataFrame(Y_np)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Plot a heatmap \n",
    "import seaborn as sns\n",
    "sns.set_theme()\n",
    "corrmat = X_df.corr()\n",
    "top_corr_features = corrmat.index\n",
    "print(top_corr_features)\n",
    "plt.figure(figsize=(10,5))\n",
    "g=sns.heatmap(X_df[top_corr_features].corr(),annot=True,cmap='RdYlGn')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Filter techniques\n",
    "\n",
    "Filter methods are a category of feature selection techniques that involve filtering out irrelevant or redundant features from the dataset before the model is built. These methods rely on statistical measures or scores to evaluate the relationship between each feature and the target variable, without involving the model's performance."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Variance Threshold \n",
    "\n",
    "Variance Threshold is a feature selection technique that focuses on filtering out features with low variance, as they may carry little information or exhibit minimal variation within the dataset. This method is particularly useful for dealing with numerical features, and it's applied prior to building a predictive model. \n",
    "\n",
    "Useful in Classification: Variance Threshold is often used in classification tasks, where low-variance features might not contribute significantly to distinguishing between classes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sklearn.feature_selection as FS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Determine the features to be included\n",
    "feature_names = X_df.columns\n",
    "print(feature_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Select features\n",
    "VarThresh = FS.VarianceThreshold(threshold=5)\n",
    "VarThresh_fit = VarThresh.fit(X_df)\n",
    "print(VarThresh_fit)\n",
    "\n",
    "print(VarThresh_fit.get_support())\n",
    "result_r = [feature_names[i] for i, n in enumerate(VarThresh_fit.get_support()) if n == 1]\n",
    "print(\"Selected Features: %s\" % result_r)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Considerations:**\n",
    "\n",
    "1. Variance Threshold is not suitable for categorical features or when interactions between features are crucial.\n",
    "\n",
    "2. It's important to scale features before applying Variance Threshold, especially if they have different scales.\n",
    "\n",
    "**Advantages:**\n",
    "\n",
    "1. Quickly removes low-variance features, reducing dimensionality.\n",
    "\n",
    "2. Can improve model performance and interpretability, especially when dealing with noisy or uninformative features.\n",
    "\n",
    "**Limitations:**\n",
    "\n",
    "1. It might remove features that, while having low variance, are still informative when combined with other features.\n",
    "\n",
    "2. Careful consideration of the threshold is required; setting it too low might exclude potentially useful features.\n",
    "\n",
    "Variance Threshold is a simple yet effective method for feature selection, especially in cases where features with little variability are likely to be noise or redundant."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### SelectKBest using ANOVA F-value \n",
    "\n",
    "SelectKBest is a feature selection technique that evaluates the relationship between each feature and the target variable using statistical tests. ANOVA F-value (Analysis of Variance F-value) is one such test used to select the top k features with the highest F-scores. This method is commonly applied in classification and regression tasks."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Change shape\n",
    "KBest_fclassif  = FS.SelectKBest(score_func=FS.f_classif,k=1)\n",
    "KBest_fclassif_fit = KBest_fclassif.fit_transform(X,Y)\n",
    "print(f'Original shape: {X.shape}, New shape: {KBest_fclassif_fit.shape}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Select features\n",
    "KBest_fclassif.fit(X,Y)\n",
    "KBest_fclassif_feature_indices = KBest_fclassif.get_support()\n",
    "KBest_fclassif_feature_names = X_df.columns[KBest_fclassif_feature_indices]\n",
    "print(\"Selected Features:\", KBest_fclassif_feature_names)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Chi-Square (χ²):\n",
    "\n",
    "The Chi-Square statistic is a measure used in statistics to assess the significance of the association between categorical variables. In the context of feature selection, the Chi-Square score is often used to evaluate the relationship between a categorical feature and a categorical target variable. It helps determine if the distribution of a feature's categories is independent of the target variable's categories or if there's a significant association."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# chi2 scores for all features\n",
    "KBest_fclassif_chi2_scores = pd.DataFrame(list(zip(feature_names,KBest_fclassif.scores_)), columns=['feature','score'])\n",
    "print(KBest_fclassif_chi2_scores)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Considerations:**\n",
    "\n",
    "1. It's crucial to specify the appropriate value of k, which determines the number of selected features.\n",
    "\n",
    "2. ANOVA F-value assumes that the features are normally distributed and have equal variances across different classes.\n",
    "\n",
    "**Advantages:**\n",
    "\n",
    "1. Selects the most relevant features based on their significance in explaining the target variable's variance.\n",
    "\n",
    "2. Can improve model performance by reducing noise and overfitting.\n",
    "\n",
    "**Limitations:**\n",
    "\n",
    "1. May not capture complex non-linear relationships between features and the target.\n",
    "2. Assumes that features are independent, which might not hold true in some cases.\n",
    "\n",
    "SelectKBest using ANOVA F-value is a valuable technique for feature selection, particularly when dealing with classification or regression problems and when linear relationships are relevant. It helps to focus on the most informative features for your model."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### SelectPercentile\n",
    "\n",
    "SelectPercentile is a feature selection technique that selects the top features based on a specified percentile of their statistical scores. This method is particularly useful when you want to retain a certain proportion of the most relevant features while discarding the rest. It can be applied with various statistical tests, such as ANOVA F-value for classification or regression tasks."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Change shape\n",
    "Percentile_fclassif  = FS.SelectPercentile(score_func=FS.f_classif)\n",
    "Percentile_fclassif_fit = Percentile_fclassif.fit_transform(X,Y)\n",
    "print(f'Original shape: {X.shape}, New shape: {Percentile_fclassif_fit.shape}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Select features \n",
    "Percentile_fclassif.fit(X,Y)\n",
    "Percentile_fclassif_feature_indices = Percentile_fclassif.get_support()\n",
    "Percentile_fclassif_feature_names = X_df.columns[Percentile_fclassif_feature_indices]\n",
    "print(\"Selected Features:\", Percentile_fclassif_feature_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# chi2 scores for all features\n",
    "Percentile_fclassif_scores = pd.DataFrame(list(zip(feature_names,Percentile_fclassif.scores_)), columns=['feature','score'])\n",
    "print(Percentile_fclassif_scores)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Considerations:**\n",
    "\n",
    "1. The percentile value directly influences the number of selected features. \n",
    "\n",
    "2. A higher percentile retains more features.\n",
    "\n",
    "**Advantages:**\n",
    "\n",
    "1. Automatically selects a proportion of top features, allowing you to balance between dimensionality reduction and information retention.\n",
    "\n",
    "2. Particularly useful when you have a large number of features and want to maintain a certain level of interpretability.\n",
    "\n",
    "**Limitations:**\n",
    "\n",
    "1. Similar to SelectKBest, SelectPercentile may not capture complex non-linear relationships and assumes feature independence.\n",
    "\n",
    "SelectPercentile is a flexible method for feature selection, giving you control over the proportion of features to retain based on their significance in explaining the target variable's variance. It helps strike a balance between model performance and dimensionality reduction."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Wrapper Technique\n",
    "\n",
    "Wrapper techniques are a category of feature selection methods that evaluate the performance of a machine learning model using a specific subset of features. Unlike filter methods that use statistical measures, wrapper techniques involve training and evaluating a model multiple times with different feature subsets."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Backward selection\n",
    "\n",
    "Backward Elimination is a specific type of wrapper technique for feature selection. It involves iteratively removing the least significant features from the original feature set until a stopping criterion is met. This method is often used in the context of regression analysis to identify the most relevant subset of features for building a predictive model. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create the model\n",
    "bwd_rfe_model = linear_model.LinearRegression()\n",
    "bwd_rfe = FS.RFE(estimator=bwd_rfe_model,n_features_to_select=3)\n",
    "bwd_rfe.fit(X, Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Apply masking\n",
    "bwd_rfe_feature_mask = bwd_rfe.support_\n",
    "print(bwd_rfe_feature_mask)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Select features\n",
    "bwd_rfe_feature_indices = np.where(bwd_rfe_feature_mask)[0]\n",
    "bwd_rfe_feature_names = feature_names[bwd_rfe_feature_indices]\n",
    "print(\"Selected Features:\",bwd_rfe_feature_names)\n",
    "# print(bwd_rfe_feature_indices)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Indices of selected features\n",
    "X_bwd_rfe = X[:, bwd_rfe_feature_indices]\n",
    "# print(X_bwd_rfe)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Considerations:**\n",
    "\n",
    "1. Statistical Significance: Backward elimination relies on statistical tests like p-values. Ensure that assumptions like normality and linearity are met for accurate significance assessment.\n",
    "\n",
    "2. Multicollinearity Impact: Removing one feature can affect the significance of others, especially in multicollinear scenarios. Removed features might be important due to correlation.\n",
    "\n",
    "3. Stopping Criteria: Choosing the right stopping point is vital. Too few features can lead to underfitting, while too many may cause overfitting. Balance based on desired model performance.\n",
    "\n",
    "4. Domain Expertise: Backward elimination may exclude theoretically important features. Consult domain knowledge to guide the process and prevent eliminating crucial variables.\n",
    "\n",
    "**Advantages:**\n",
    "\n",
    "1. Backward elimination provides a systematic way to identify the most important features for a predictive model.\n",
    "\n",
    "2. It can help in simplifying the model and improving its interpretability.\n",
    "\n",
    "**Limitations:**\n",
    "\n",
    "1. The process can be computationally intensive, especially with a large number of features.\n",
    "\n",
    "2. Backward elimination may not capture complex interactions between features, and it assumes that the model's performance improves with fewer features.\n",
    "\n",
    "Backward elimination is a structured approach for selecting features based on their significance in a model. While it has limitations, such as assuming linear relationships and interactions, it can be a useful tool in achieving model parsimony and interpretability."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### RFE (Backward selection method)\n",
    "\n",
    "Recursive Feature Elimination (RFE) is a specific type of backward selection method for feature selection. It iteratively removes the least significant features from the model while evaluating its performance. RFE is particularly useful when building predictive models and aims to identify the most relevant subset of features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "rfe_mod = FS.RFE(linear_model.LinearRegression(),n_features_to_select=3)\n",
    "rfe_values=rfe_mod.fit(X,Y)\n",
    "rfe_values.support_\n",
    "rfe_values.ranking_\n",
    "print(\"No. of Selected Features: %s\" % (rfe_values.n_features_))\n",
    "print(\"Selected Features mask: %s\" % (rfe_values.support_))\n",
    "print(\"Selected Features ranking: %s\" % (rfe_values.ranking_))\n",
    "result_rfe = [feature_names[i] for i, n in enumerate(rfe_values.support_) if n == 1]\n",
    "print(\"Selected Features: %s\" % result_rfe)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Considerations:**\n",
    "\n",
    "1. Choose an appropriate estimator for RFE that matches the problem type (classification, regression, etc.).\n",
    "\n",
    "2. Balance the number of features retained with model complexity and performance.\n",
    "\n",
    "**Advantages:**\n",
    "\n",
    "1. RFE systematically evaluates feature importance, capturing complex interactions and dependencies.\n",
    "\n",
    "2. It can lead to improved model performance and interpretability by selecting only relevant features.\n",
    "\n",
    "**Limitations:**\n",
    "\n",
    "1. RFE can be computationally intensive, especially with a large number of features.\n",
    "\n",
    "2. Model instability due to feature elimination might require careful parameter tuning.\n",
    "\n",
    "RFE is a robust method for feature selection, suitable for a wide range of modeling techniques. It helps in identifying the most important features by iteratively evaluating their contribution to the model's performance."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### RFE with Cross-Validation\n",
    "\n",
    "Recursive Feature Elimination with Cross-Validation (RFECV) is an advanced version of the RFE method that incorporates cross-validation during the feature selection process. It addresses some of the limitations of standard RFE by providing a more reliable and robust estimate of feature importance."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### RFE with Cross-Validation (Method 1)\n",
    "\n",
    "This method is similar to the **\"Backward selection\"** that we have performed previously."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create the model\n",
    "bwd_rfecv_model = linear_model.LinearRegression()\n",
    "bwd_rfecv = FS.RFECV(estimator=bwd_rfecv_model, cv=2)\n",
    "bwd_rfecv.fit(X, Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Apply masking\n",
    "bwd_rfecv_feature_mask = bwd_rfecv.support_\n",
    "print(bwd_rfecv_feature_mask)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Select features\n",
    "bwd_rfecv_feature_indices = np.where(bwd_rfecv_feature_mask)[0]\n",
    "bwd_rfecv_feature_names = feature_names[bwd_rfecv_feature_indices]\n",
    "print(\"Selected Features:\",bwd_rfecv_feature_names)\n",
    "# print(bwd_rfecv_feature_indices)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Indices of selected features\n",
    "X_bwd_rfecv = X[:, bwd_rfecv_feature_indices]\n",
    "# print(X_bwd_rfecv)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### RFE with Cross-Validation (Method 2)\n",
    "\n",
    "This method is similar to the **\"RFE (Backward selection method)\"** that we have performed previously. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "rfecv_mod = FS.RFECV(linear_model.LinearRegression(),cv=2)\n",
    "rfecv_values=rfecv_mod.fit(X,Y)\n",
    "rfecv_values.support_\n",
    "rfecv_values.ranking_\n",
    "print(\"No. of Selected Features: %s\" % (rfecv_values.n_features_))\n",
    "print(\"Selected Features mask: %s\" % (rfecv_values.support_))\n",
    "print(\"Selected Features ranking: %s\" % (rfecv_values.ranking_))\n",
    "result_rfecv = [feature_names[i] for i, n in enumerate(rfecv_values.support_) if n == 1]\n",
    "print(\"Selected Features: %s\" % result_rfecv)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Considerations:**\n",
    "\n",
    "1. Choose an appropriate estimator for RFECV that matches the problem type (classification, regression, etc.).\n",
    "\n",
    "2. Specify the cross-validation strategy (cv) and scoring metric (scoring) according to your dataset and modeling objectives.\n",
    "\n",
    "**Advantages:**\n",
    "\n",
    "1. RFECV offers more reliable feature selection by incorporating cross-validation to estimate feature importance.\n",
    "\n",
    "2. It helps mitigate overfitting and enhances model generalization.\n",
    "\n",
    "**Limitations:**\n",
    "\n",
    "1. RFECV can be computationally intensive due to repeated cross-validation iterations.\n",
    "\n",
    "2. Model instability due to feature elimination might require careful parameter tuning.\n",
    "\n",
    "RFECV is a powerful feature selection technique that takes into account both feature importance and model performance via cross-validation. It provides a more accurate estimate of feature relevance and contributes to building more robust predictive models."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#  Implications and Applications:\n",
    "\n",
    "Optimal model selection has profound implications for real-world decision-making, shaping a wide spectrum of industries and applications. By choosing the most suitable predictive model, businesses and organizations can significantly enhance their operations and outcomes. The accurate predictions facilitated by optimal model selection lead to heightened accuracy and performance, which is critical in fields such as finance, healthcare, and manufacturing.\n",
    "\n",
    "One of the key benefits is resource optimization. Industries like supply chain management, energy distribution, and agriculture benefit from precise predictions that reduce inefficiencies and wastage, thereby contributing to cost reduction. Moreover, optimal model selection aids in risk mitigation, enabling better assessment and management of potential threats in fields like insurance and finance.\n",
    "\n",
    "In healthcare, optimal models play a pivotal role in medical diagnosis, treatment selection, and prognosis, ultimately leading to improved patient outcomes. The environmental impact is substantial as well, with accurate predictions guiding disaster preparedness, ecosystem conservation, and climate change responses.\n",
    "\n",
    "Across domains, optimal model selection has transformative effects on scientific research, public policy, customer experiences, and educational advancements. It empowers decision-makers with data-driven insights, fostering effective governance, strategic planning, and innovation. Ultimately, optimal model selection is a cornerstone of modern decision-making, shaping a more efficient, informed, and impactful world."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Conclusion:\n",
    "\n",
    "In conclusion, the study underscores several pivotal takeaways that emphasize the critical role of optimal regression model selection in data analysis and decision-making. The process of identifying and choosing the most suitable model holds profound implications for real-world applications.\n",
    "\n",
    "First and foremost, optimal model selection directly contributes to heightened accuracy and predictive performance. Through a systematic evaluation of various regression techniques, the study highlights the potential for achieving more precise and reliable predictions across diverse domains. This accuracy translates into enhanced resource allocation, cost reduction, and risk management, empowering organizations to make informed decisions.\n",
    "\n",
    "Furthermore, the study underscores the value of resource optimization. By carefully selecting the optimal regression model, industries can streamline operations, reduce inefficiencies, and ensure the optimal allocation of valuable resources. This resonates particularly in sectors such as manufacturing, finance, and healthcare.\n",
    "\n",
    "Additionally, the study's findings emphasize that optimal regression model selection is not a mere technical step, but rather a strategic imperative. The right model not only improves accuracy but also facilitates better interpretability and understanding of underlying relationships within the data.\n",
    "\n",
    "In essence, the study affirms that optimal regression model selection is a linchpin of modern data analysis. Its impact spans across industries and applications, from enhancing decision-making processes to driving innovation. By embracing the principles and methodologies outlined in this study, organizations can harness the power of data-driven insights and achieve a competitive edge in today's complex and dynamic landscape."
   ]
  }
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