01-Introductory-Machine-Learning.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "364a84ad",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from scipy import stats\n",
    "import matplotlib.pyplot as plt\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "40d926b4",
   "metadata": {},
   "source": [
    "# 01- Mean, Median, and Mode\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "890680df",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mean:  89.76923076923077\n",
      "median:  87.0\n",
      "mode:  ModeResult(mode=array([86]), count=array([3]))\n"
     ]
    }
   ],
   "source": [
    "array = [99,86,87,88,111,86,103,87,94,78,77,85,86]\n",
    "print(\"mean: \", np.mean(array))\n",
    "\n",
    "array = [99,86,87,88,111,86,103,87,94,78,77,85,86]\n",
    "# array.sort()\n",
    "print(\"median: \", np.median(array))\n",
    "\n",
    "\n",
    "array = [99,86,87,88,111,86,103,87,94,78,77,85,86]\n",
    "print(\"mode: \", stats.mode(array))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "01583c87",
   "metadata": {},
   "source": [
    "# 02- Standard Deviation and Variance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "e693b2f3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "std:  37.84501153334721\n",
      "Variance:  1432.2448979591834\n"
     ]
    }
   ],
   "source": [
    "array = [32,111,138,28,59,77,97]\n",
    "print(\"std: \", np.std(array))\n",
    "\n",
    "array = [32,111,138,28,59,77,97]\n",
    "print(\"Variance: \", np.var(array))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "314be9d3",
   "metadata": {},
   "source": [
    "# 03- Percentiles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "b9367a39",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "age that 75% of the people are younger than:  43.0\n"
     ]
    }
   ],
   "source": [
    "ages = [5,31,43,48,50,41,7,11,15,39,80,82,32,2,8,6,25,36,27,61,31]\n",
    "\n",
    "#What is the age that 75% of the people are younger than?\n",
    "print(\"age that 75% of the people are younger than: \",np.percentile(ages, 75))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aad523b4",
   "metadata": {},
   "source": [
    "# 04- Data Distribution\n",
    "create a completely random array, of a given size, and between two given values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "0aaa2376",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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vJL6+TeGOxSyGvl/10Ik2r/1J4ONV9alp1zNJVfUMcD9w5ZRLWUlXAG9u89t3AG9I8q/TLWkyqupo+30c+DcG09ZjMYuh71c9dKC9qbkHOFhVH5p2PZOQZC7J+rb8EgYXK3xzqkWtoKp6f1Vtrqp5Bs/j+6rqj6dc1opLcl67OIEk5wFvBMZ2Zd7MhX5VPQ+c+KqHg8CdK/xVD1OX5HbgP4BXJzmSZMe0a5qAK4B3MDj7e6T9XD3tolbYRuD+JF9jcHJzT1V1cxljRzYAX0zyVeBLwGeq6nPj2vnMXbIpSTq9mTvTlySdnqEvSR0x9CWpI4a+JHXE0Jekjhj6ktQRQ1+SOvJ/2/48V8H1RhoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "array = np.random.uniform(0.0, 5.0, 100000)\n",
    "\n",
    "plt.hist(array, 100)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "59f84d44",
   "metadata": {},
   "source": [
    "# 05- Normal Data Distribution\n",
    "\n",
    "create an array where the values are concentrated around a given value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "7fd63a31",
   "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": [
    "array = np.random.normal(5.0, 1.0, 100000)\n",
    "\n",
    "plt.hist(array, 100)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d960eeed",
   "metadata": {},
   "source": [
    "# 06-  Scatter Plot\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "810cd17b",
   "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": [
    "#array with the mean of 5.0 and standard deviation of 1.0.\n",
    "x = np.random.normal(5.0, 1.5, 1000)\n",
    "\n",
    "#array with the mean of 10.0 and standard deviation of 2.0\n",
    "y = np.random.normal(10.0, 2.0, 1000)\n",
    "\n",
    "plt.scatter(x, y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "731fa4f4",
   "metadata": {},
   "source": [
    "# 07- Linear Regression\n",
    "Linear regression uses the relationship between the data-points to draw a straight line through all them"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "6d0e57f3",
   "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"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "85.59308314937454\n"
     ]
    }
   ],
   "source": [
    "x = [5,7,8,7,2,17,2,9,4,11,12,9,6]\n",
    "y = [99,86,87,88,111,86,103,87,94,78,77,85,86]\n",
    "\n",
    "'''\n",
    "Execute a method that returns some important key values of Linear Regression\n",
    "\n",
    "relationship - the coefficient of correlation - is called r: The r value ranges from -1 to 1, \n",
    "where 0 means no relationship, and 1 (and -1) means 100% related\n",
    "\n",
    "'''\n",
    "\n",
    "slope, intercept, r, p, std_err = stats.linregress(x, y)\n",
    "\n",
    "def myfunc(x):\n",
    "  return slope * x + intercept\n",
    "\n",
    "mymodel = list(map(myfunc, x))\n",
    "plt.scatter(x, y)\n",
    "plt.plot(x, mymodel)\n",
    "plt.show()\n",
    "\n",
    "#Predict\n",
    "speed = myfunc(10)\n",
    "print(speed)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ca730677",
   "metadata": {},
   "source": [
    "# 08- Polynomial Regression\n",
    "uses the relationship between the variables x and y to find the best way to draw a line through the data points"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "ff17f854",
   "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": [
    "x = [1,2,3,5,6,7,8,9,10,12,13,14,15,16,18,19,21,22]\n",
    "y = [100,90,80,60,60,55,60,65,70,70,75,76,78,79,90,99,99,100]\n",
    "\n",
    "model = np.poly1d(np.polyfit(x, y, 3))\n",
    "\n",
    "line = np.linspace(1, 22, 100)\n",
    "\n",
    "plt.scatter(x, y)\n",
    "plt.plot(line, model(line))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d0d038a5",
   "metadata": {},
   "source": [
    "# 09- Multiple Regression\n",
    "like linear regression, but with more than one independent value, meaning that we try to predict a value based on two or more variables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "9c5b20b4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[107.2087328]\n",
      "[0.00755095 0.00780526]\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from sklearn import linear_model\n",
    "\n",
    "#Pandas module allows us to read csv files and return a DataFrame object\n",
    "DataFrame = pd.read_csv(\"cars.csv\")\n",
    "\n",
    "X = DataFrame[['Weight', 'Volume']]\n",
    "y = DataFrame['CO2']\n",
    "\n",
    "regr = linear_model.LinearRegression()\n",
    "regr.fit(X, y)\n",
    "\n",
    "#predict the CO2 emission of a car where the weight is 2300kg, and the volume is 1300cm3:\n",
    "predicted = regr.predict([[2300, 1300]])\n",
    "print(predicted)\n",
    "\n",
    "#coefficient is a factor that describes the relationship with an unknown variable\n",
    "'''\n",
    "In this case, we can ask for the coefficient value of weight against CO2, and for volume against CO2.\n",
    "The answer(s) we get tells us what would happen if we increase,\n",
    "or decrease, one of the independent values.\n",
    "'''\n",
    "\n",
    "print(regr.coef_)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "180a6226",
   "metadata": {},
   "source": [
    "# 10- Scale\n",
    "scale data into new values that are easier to compare\n",
    "\n",
    "standardization method uses this formula:\n",
    "\n",
    "z = (x - u) / s\n",
    "\n",
    "Where z is the new value, x is the original value,  is the mean and s is the standard deviation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "5d8992a4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 4.22104928 -0.81116837]]\n",
      "[107.2087328]\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from sklearn import linear_model\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "\n",
    "scale = StandardScaler()\n",
    "\n",
    "DataFrame = pd.read_csv(\"cars.csv\")\n",
    "\n",
    "X = DataFrame[['Weight', 'Volume']]\n",
    "y = DataFrame['CO2']\n",
    "scaledX = scale.fit_transform(X)\n",
    "# print(scaledX)\n",
    "\n",
    "regr = linear_model.LinearRegression()\n",
    "regr.fit(scaledX, y)\n",
    "\n",
    "#predict\n",
    "scaled = scale.transform([[2300, 1300]])\n",
    "print(scaled)\n",
    "\n",
    "predicted = regr.predict([scaled[0]])\n",
    "print(predicted)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6b800976",
   "metadata": {},
   "source": [
    "# 11- Train/Test\n",
    "\n",
    "\n",
    "#### 11-1- polynomial regression line through the data points"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "82430b3d",
   "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": [
    "np.random.seed(2)\n",
    "\n",
    "x = np.random.normal(3, 1, 100)\n",
    "y = np.random.normal(150, 40, 100) / x\n",
    "\n",
    "train_x = x[:80]\n",
    "train_y = y[:80]\n",
    "\n",
    "test_x = x[80:]\n",
    "test_y = y[80:]\n",
    "\n",
    "#Draw a polynomial regression line through the data point\n",
    "\n",
    "mymodel = np.poly1d(np.polyfit(train_x, train_y, 4))\n",
    "\n",
    "myline = np.linspace(0, 6, 100)\n",
    "\n",
    "plt.scatter(train_x, train_y)\n",
    "plt.plot(myline, mymodel(myline))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cec064b6",
   "metadata": {},
   "source": [
    "#### 11-2-  R2 or R-squared\n",
    "\n",
    "It measures the relationship between the x axis and the y axis, and the value ranges from 0 to 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "056ca8ad",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.735999316871548\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import r2_score\n",
    "\n",
    "np.random.seed(2)\n",
    "\n",
    "x = np.random.normal(3, 1, 100)\n",
    "y = np.random.normal(150, 40, 100) / x\n",
    "\n",
    "train_x = x[:80]\n",
    "train_y = y[:80]\n",
    "\n",
    "test_x = x[80:]\n",
    "test_y = y[80:]\n",
    "\n",
    "mymodel = np.poly1d(np.polyfit(train_x, train_y, 3))\n",
    "\n",
    "r2 = r2_score(train_y, mymodel(train_x))\n",
    "\n",
    "print(r2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3677e6f2",
   "metadata": {},
   "source": [
    "#### 11-3- R2 score when using testing data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "c2bea441",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8086921460343559\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import r2_score\n",
    "\n",
    "\n",
    "np.random.seed(2)\n",
    "\n",
    "x = np.random.normal(3, 1, 100)\n",
    "y = np.random.normal(150, 40, 100) / x\n",
    "\n",
    "train_x = x[:80]\n",
    "train_y = y[:80]\n",
    "\n",
    "test_x = x[80:]\n",
    "test_y = y[80:]\n",
    "\n",
    "mymodel = np.poly1d(np.polyfit(train_x, train_y, 4))\n",
    "\n",
    "r2 = r2_score(test_y, mymodel(test_x))\n",
    "\n",
    "print(r2)"
   ]
  }
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