HW/HW3/Week3-Classification.ipynb

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     "text": [
      "Intel(R) Extension for Scikit-learn* enabled (https://github.com/intel/scikit-learn-intelex)\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import wget\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline  \n",
    "from sklearnex import patch_sklearn, config_context\n",
    "patch_sklearn()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ccfd7b44",
   "metadata": {},
   "source": [
    "### Dataset\n",
    "\n",
    "In this homework, we will use the California Housing Prices data from [Kaggle](https://www.kaggle.com/datasets/camnugent/california-housing-prices).\n",
    "\n",
    "Here's a wget-able [link](https://raw.githubusercontent.com/alexeygrigorev/datasets/master/housing.csv):\n",
    "\n",
    "```bash\n",
    "wget https://raw.githubusercontent.com/alexeygrigorev/datasets/master/housing.csv\n",
    "```\n",
    "We'll keep working with the `'median_house_value'` variable, and we'll transform it to a classification task. \n",
    " \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "1eb7719a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100% [..........................................................................] 1423529 / 1423529"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'housing.csv'"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wget.download(\"https://raw.githubusercontent.com/alexeygrigorev/datasets/master/housing.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5d02e71c",
   "metadata": {},
   "source": [
    "### Features\n",
    "\n",
    "For the rest of the homework, you'll need to use only these columns:\n",
    "\n",
    "* `'latitude'`,\n",
    "* `'longitude'`,\n",
    "* `'housing_median_age'`,\n",
    "* `'total_rooms'`,\n",
    "* `'total_bedrooms'`,\n",
    "* `'population'`,\n",
    "* `'households'`,\n",
    "* `'median_income'`,\n",
    "* `'median_house_value'`,\n",
    "* `'ocean_proximity'`,\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "ebfb582b",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>median_house_value</th>\n",
       "      <th>ocean_proximity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-122.23</td>\n",
       "      <td>37.88</td>\n",
       "      <td>41.0</td>\n",
       "      <td>880.0</td>\n",
       "      <td>129.0</td>\n",
       "      <td>322.0</td>\n",
       "      <td>126.0</td>\n",
       "      <td>8.3252</td>\n",
       "      <td>452600.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-122.22</td>\n",
       "      <td>37.86</td>\n",
       "      <td>21.0</td>\n",
       "      <td>7099.0</td>\n",
       "      <td>1106.0</td>\n",
       "      <td>2401.0</td>\n",
       "      <td>1138.0</td>\n",
       "      <td>8.3014</td>\n",
       "      <td>358500.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-122.24</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1467.0</td>\n",
       "      <td>190.0</td>\n",
       "      <td>496.0</td>\n",
       "      <td>177.0</td>\n",
       "      <td>7.2574</td>\n",
       "      <td>352100.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1274.0</td>\n",
       "      <td>235.0</td>\n",
       "      <td>558.0</td>\n",
       "      <td>219.0</td>\n",
       "      <td>5.6431</td>\n",
       "      <td>341300.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1627.0</td>\n",
       "      <td>280.0</td>\n",
       "      <td>565.0</td>\n",
       "      <td>259.0</td>\n",
       "      <td>3.8462</td>\n",
       "      <td>342200.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
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      "text/plain": [
       "   longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
       "0    -122.23     37.88                41.0        880.0           129.0   \n",
       "1    -122.22     37.86                21.0       7099.0          1106.0   \n",
       "2    -122.24     37.85                52.0       1467.0           190.0   \n",
       "3    -122.25     37.85                52.0       1274.0           235.0   \n",
       "4    -122.25     37.85                52.0       1627.0           280.0   \n",
       "\n",
       "   population  households  median_income  median_house_value ocean_proximity  \n",
       "0       322.0       126.0         8.3252            452600.0        NEAR BAY  \n",
       "1      2401.0      1138.0         8.3014            358500.0        NEAR BAY  \n",
       "2       496.0       177.0         7.2574            352100.0        NEAR BAY  \n",
       "3       558.0       219.0         5.6431            341300.0        NEAR BAY  \n",
       "4       565.0       259.0         3.8462            342200.0        NEAR BAY  "
      ]
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   ],
   "source": [
    "select_cols = [\"latitude\", \"longitude\", \"housing_median_age\", \"total_rooms\", \"total_bedrooms\", \"population\", \"households\", \n",
    "               \"median_income\", \"median_house_value\", \"ocean_proximity\"]\n",
    "               \n",
    "csv_path = \"./housing.csv\"\n",
    "df = pd.read_csv(csv_path, usecols=select_cols)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5761f902",
   "metadata": {},
   "source": [
    "### some basic data cleaning:\n",
    "\n",
    "1. We've only selected columns of interest. Reduces memory footprint. \n",
    "2. Replacing upper case with lower case, and replacing spaces with underscores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "8fa75600",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'df' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Input \u001b[1;32mIn [2]\u001b[0m, in \u001b[0;36m<cell line: 1>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[43mdf\u001b[49m\u001b[38;5;241m.\u001b[39minfo()\n",
      "\u001b[1;31mNameError\u001b[0m: name 'df' is not defined"
     ]
    }
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   "source": [
    "df.info()"
   ]
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   "cell_type": "code",
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   "id": "f6c4bd36",
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   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>median_house_value</th>\n",
       "      <th>ocean_proximity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-122.23</td>\n",
       "      <td>37.88</td>\n",
       "      <td>41.0</td>\n",
       "      <td>880.0</td>\n",
       "      <td>129.0</td>\n",
       "      <td>322.0</td>\n",
       "      <td>126.0</td>\n",
       "      <td>8.3252</td>\n",
       "      <td>452600.0</td>\n",
       "      <td>near_bay</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-122.22</td>\n",
       "      <td>37.86</td>\n",
       "      <td>21.0</td>\n",
       "      <td>7099.0</td>\n",
       "      <td>1106.0</td>\n",
       "      <td>2401.0</td>\n",
       "      <td>1138.0</td>\n",
       "      <td>8.3014</td>\n",
       "      <td>358500.0</td>\n",
       "      <td>near_bay</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-122.24</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1467.0</td>\n",
       "      <td>190.0</td>\n",
       "      <td>496.0</td>\n",
       "      <td>177.0</td>\n",
       "      <td>7.2574</td>\n",
       "      <td>352100.0</td>\n",
       "      <td>near_bay</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1274.0</td>\n",
       "      <td>235.0</td>\n",
       "      <td>558.0</td>\n",
       "      <td>219.0</td>\n",
       "      <td>5.6431</td>\n",
       "      <td>341300.0</td>\n",
       "      <td>near_bay</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1627.0</td>\n",
       "      <td>280.0</td>\n",
       "      <td>565.0</td>\n",
       "      <td>259.0</td>\n",
       "      <td>3.8462</td>\n",
       "      <td>342200.0</td>\n",
       "      <td>near_bay</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>919.0</td>\n",
       "      <td>213.0</td>\n",
       "      <td>413.0</td>\n",
       "      <td>193.0</td>\n",
       "      <td>4.0368</td>\n",
       "      <td>269700.0</td>\n",
       "      <td>near_bay</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.84</td>\n",
       "      <td>52.0</td>\n",
       "      <td>2535.0</td>\n",
       "      <td>489.0</td>\n",
       "      <td>1094.0</td>\n",
       "      <td>514.0</td>\n",
       "      <td>3.6591</td>\n",
       "      <td>299200.0</td>\n",
       "      <td>near_bay</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.84</td>\n",
       "      <td>52.0</td>\n",
       "      <td>3104.0</td>\n",
       "      <td>687.0</td>\n",
       "      <td>1157.0</td>\n",
       "      <td>647.0</td>\n",
       "      <td>3.1200</td>\n",
       "      <td>241400.0</td>\n",
       "      <td>near_bay</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>-122.26</td>\n",
       "      <td>37.84</td>\n",
       "      <td>42.0</td>\n",
       "      <td>2555.0</td>\n",
       "      <td>665.0</td>\n",
       "      <td>1206.0</td>\n",
       "      <td>595.0</td>\n",
       "      <td>2.0804</td>\n",
       "      <td>226700.0</td>\n",
       "      <td>near_bay</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.84</td>\n",
       "      <td>52.0</td>\n",
       "      <td>3549.0</td>\n",
       "      <td>707.0</td>\n",
       "      <td>1551.0</td>\n",
       "      <td>714.0</td>\n",
       "      <td>3.6912</td>\n",
       "      <td>261100.0</td>\n",
       "      <td>near_bay</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
       "0    -122.23     37.88                41.0        880.0           129.0   \n",
       "1    -122.22     37.86                21.0       7099.0          1106.0   \n",
       "2    -122.24     37.85                52.0       1467.0           190.0   \n",
       "3    -122.25     37.85                52.0       1274.0           235.0   \n",
       "4    -122.25     37.85                52.0       1627.0           280.0   \n",
       "5    -122.25     37.85                52.0        919.0           213.0   \n",
       "6    -122.25     37.84                52.0       2535.0           489.0   \n",
       "7    -122.25     37.84                52.0       3104.0           687.0   \n",
       "8    -122.26     37.84                42.0       2555.0           665.0   \n",
       "9    -122.25     37.84                52.0       3549.0           707.0   \n",
       "\n",
       "   population  households  median_income  median_house_value ocean_proximity  \n",
       "0       322.0       126.0         8.3252            452600.0        near_bay  \n",
       "1      2401.0      1138.0         8.3014            358500.0        near_bay  \n",
       "2       496.0       177.0         7.2574            352100.0        near_bay  \n",
       "3       558.0       219.0         5.6431            341300.0        near_bay  \n",
       "4       565.0       259.0         3.8462            342200.0        near_bay  \n",
       "5       413.0       193.0         4.0368            269700.0        near_bay  \n",
       "6      1094.0       514.0         3.6591            299200.0        near_bay  \n",
       "7      1157.0       647.0         3.1200            241400.0        near_bay  \n",
       "8      1206.0       595.0         2.0804            226700.0        near_bay  \n",
       "9      1551.0       714.0         3.6912            261100.0        near_bay  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# for column names:\n",
    "\n",
    "df.columns = df.columns.str.lower().str.replace(' ', '_')\n",
    "\n",
    "# for data in the dataframe:\n",
    "\n",
    "strings = list(df.dtypes[df.dtypes == 'object'].index)\n",
    "for col in strings:\n",
    "    df[col] = df[col].str.lower().str.replace(' ', '_')\n",
    "\n",
    "df.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c0499f11",
   "metadata": {},
   "source": [
    "3. Leaving dtype conversions of each column\n",
    "4. Moving on to data visualization\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "0ff575a7",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Mona\\AppData\\Local\\Temp\\ipykernel_15788\\726926412.py:5: UserWarning: To output multiple subplots, the figure containing the passed axes is being cleared.\n",
      "  df.hist(bins=100, ax=ax, layout=(3, 3), column=[\"longitude\", \"latitude\", \"housing_median_age\", \"total_rooms\", \"total_bedrooms\",\n"
     ]
    },
    {
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      "text/plain": [
       "<Figure size 1500x1000 with 9 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# INSPECTING HISTOGRAM:\n",
    "\n",
    "fig = plt.figure(figsize=(15,10))\n",
    "ax = plt.gca()\n",
    "df.hist(bins=100, ax=ax, layout=(3, 3), column=[\"longitude\", \"latitude\", \"housing_median_age\", \"total_rooms\", \"total_bedrooms\", \n",
    "                                                  \"population\", \"households\", \"median_income\", \"median_house_value\"])\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "477f032d",
   "metadata": {},
   "source": [
    "Target variable median_house_value has a tail. Rest relatively normal, except median income and median house age."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "38486425",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>median_house_value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>20640.00</td>\n",
       "      <td>20640.00</td>\n",
       "      <td>20640.00</td>\n",
       "      <td>20640.00</td>\n",
       "      <td>20433.00</td>\n",
       "      <td>20640.00</td>\n",
       "      <td>20640.00</td>\n",
       "      <td>20640.00</td>\n",
       "      <td>20640.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>-119.57</td>\n",
       "      <td>35.63</td>\n",
       "      <td>28.64</td>\n",
       "      <td>2635.76</td>\n",
       "      <td>537.87</td>\n",
       "      <td>1425.48</td>\n",
       "      <td>499.54</td>\n",
       "      <td>3.87</td>\n",
       "      <td>206855.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.00</td>\n",
       "      <td>2.14</td>\n",
       "      <td>12.59</td>\n",
       "      <td>2181.62</td>\n",
       "      <td>421.39</td>\n",
       "      <td>1132.46</td>\n",
       "      <td>382.33</td>\n",
       "      <td>1.90</td>\n",
       "      <td>115395.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-124.35</td>\n",
       "      <td>32.54</td>\n",
       "      <td>1.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.50</td>\n",
       "      <td>14999.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-121.80</td>\n",
       "      <td>33.93</td>\n",
       "      <td>18.00</td>\n",
       "      <td>1447.75</td>\n",
       "      <td>296.00</td>\n",
       "      <td>787.00</td>\n",
       "      <td>280.00</td>\n",
       "      <td>2.56</td>\n",
       "      <td>119600.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>-118.49</td>\n",
       "      <td>34.26</td>\n",
       "      <td>29.00</td>\n",
       "      <td>2127.00</td>\n",
       "      <td>435.00</td>\n",
       "      <td>1166.00</td>\n",
       "      <td>409.00</td>\n",
       "      <td>3.53</td>\n",
       "      <td>179700.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>-118.01</td>\n",
       "      <td>37.71</td>\n",
       "      <td>37.00</td>\n",
       "      <td>3148.00</td>\n",
       "      <td>647.00</td>\n",
       "      <td>1725.00</td>\n",
       "      <td>605.00</td>\n",
       "      <td>4.74</td>\n",
       "      <td>264725.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>-114.31</td>\n",
       "      <td>41.95</td>\n",
       "      <td>52.00</td>\n",
       "      <td>39320.00</td>\n",
       "      <td>6445.00</td>\n",
       "      <td>35682.00</td>\n",
       "      <td>6082.00</td>\n",
       "      <td>15.00</td>\n",
       "      <td>500001.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
       "count   20640.00  20640.00            20640.00     20640.00        20433.00   \n",
       "mean     -119.57     35.63               28.64      2635.76          537.87   \n",
       "std         2.00      2.14               12.59      2181.62          421.39   \n",
       "min      -124.35     32.54                1.00         2.00            1.00   \n",
       "25%      -121.80     33.93               18.00      1447.75          296.00   \n",
       "50%      -118.49     34.26               29.00      2127.00          435.00   \n",
       "75%      -118.01     37.71               37.00      3148.00          647.00   \n",
       "max      -114.31     41.95               52.00     39320.00         6445.00   \n",
       "\n",
       "       population  households  median_income  median_house_value  \n",
       "count    20640.00    20640.00       20640.00            20640.00  \n",
       "mean      1425.48      499.54           3.87           206855.82  \n",
       "std       1132.46      382.33           1.90           115395.62  \n",
       "min          3.00        1.00           0.50            14999.00  \n",
       "25%        787.00      280.00           2.56           119600.00  \n",
       "50%       1166.00      409.00           3.53           179700.00  \n",
       "75%       1725.00      605.00           4.74           264725.00  \n",
       "max      35682.00     6082.00          15.00           500001.00  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe().round(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e2298b52",
   "metadata": {},
   "source": [
    "### Data preparation\n",
    "\n",
    "* Select only the features from above and fill in the missing values with 0.\n",
    "* Create a new column `rooms_per_household` by dividing the column `total_rooms` by the column `households` from dataframe. \n",
    "* Create a new column `bedrooms_per_room` by dividing the column `total_bedrooms` by the column `total_rooms` from dataframe. \n",
    "* Create a new column `population_per_household` by dividing the column `population` by the column `households` from dataframe. \n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "6d2d6f55",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "longitude               0\n",
       "latitude                0\n",
       "housing_median_age      0\n",
       "total_rooms             0\n",
       "total_bedrooms        207\n",
       "population              0\n",
       "households              0\n",
       "median_income           0\n",
       "median_house_value      0\n",
       "ocean_proximity         0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking if any missing values exist in any of the missing columns:\n",
    "df.isna().sum(axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "5524f129",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "longitude             0\n",
       "latitude              0\n",
       "housing_median_age    0\n",
       "total_rooms           0\n",
       "total_bedrooms        0\n",
       "population            0\n",
       "households            0\n",
       "median_income         0\n",
       "median_house_value    0\n",
       "ocean_proximity       0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "column_names = df.columns[df.isnull().any()].to_list()\n",
    "\n",
    "for column in column_names:\n",
    "    df[column].fillna(0, inplace=True) \n",
    "    \n",
    "df.isna().sum(axis=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "242e5ec5",
   "metadata": {},
   "source": [
    "Creating new columns: \n",
    "\n",
    "* Create a new column `rooms_per_household` by dividing the column `total_rooms` by the column `households` from dataframe. \n",
    "* Create a new column `bedrooms_per_room` by dividing the column `total_bedrooms` by the column `total_rooms` from dataframe. \n",
    "* Create a new column `population_per_household` by dividing the column `population` by the column `households` from dataframe. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "87652273",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>median_house_value</th>\n",
       "      <th>ocean_proximity</th>\n",
       "      <th>rooms_per_household</th>\n",
       "      <th>bedrooms_per_room</th>\n",
       "      <th>population_per_household</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-122.23</td>\n",
       "      <td>37.88</td>\n",
       "      <td>41.0</td>\n",
       "      <td>880.0</td>\n",
       "      <td>129.0</td>\n",
       "      <td>322.0</td>\n",
       "      <td>126.0</td>\n",
       "      <td>8.3252</td>\n",
       "      <td>452600.0</td>\n",
       "      <td>near_bay</td>\n",
       "      <td>6.984127</td>\n",
       "      <td>0.146591</td>\n",
       "      <td>2.555556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-122.22</td>\n",
       "      <td>37.86</td>\n",
       "      <td>21.0</td>\n",
       "      <td>7099.0</td>\n",
       "      <td>1106.0</td>\n",
       "      <td>2401.0</td>\n",
       "      <td>1138.0</td>\n",
       "      <td>8.3014</td>\n",
       "      <td>358500.0</td>\n",
       "      <td>near_bay</td>\n",
       "      <td>6.238137</td>\n",
       "      <td>0.155797</td>\n",
       "      <td>2.109842</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-122.24</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1467.0</td>\n",
       "      <td>190.0</td>\n",
       "      <td>496.0</td>\n",
       "      <td>177.0</td>\n",
       "      <td>7.2574</td>\n",
       "      <td>352100.0</td>\n",
       "      <td>near_bay</td>\n",
       "      <td>8.288136</td>\n",
       "      <td>0.129516</td>\n",
       "      <td>2.802260</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1274.0</td>\n",
       "      <td>235.0</td>\n",
       "      <td>558.0</td>\n",
       "      <td>219.0</td>\n",
       "      <td>5.6431</td>\n",
       "      <td>341300.0</td>\n",
       "      <td>near_bay</td>\n",
       "      <td>5.817352</td>\n",
       "      <td>0.184458</td>\n",
       "      <td>2.547945</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1627.0</td>\n",
       "      <td>280.0</td>\n",
       "      <td>565.0</td>\n",
       "      <td>259.0</td>\n",
       "      <td>3.8462</td>\n",
       "      <td>342200.0</td>\n",
       "      <td>near_bay</td>\n",
       "      <td>6.281853</td>\n",
       "      <td>0.172096</td>\n",
       "      <td>2.181467</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
       "0    -122.23     37.88                41.0        880.0           129.0   \n",
       "1    -122.22     37.86                21.0       7099.0          1106.0   \n",
       "2    -122.24     37.85                52.0       1467.0           190.0   \n",
       "3    -122.25     37.85                52.0       1274.0           235.0   \n",
       "4    -122.25     37.85                52.0       1627.0           280.0   \n",
       "\n",
       "   population  households  median_income  median_house_value ocean_proximity  \\\n",
       "0       322.0       126.0         8.3252            452600.0        near_bay   \n",
       "1      2401.0      1138.0         8.3014            358500.0        near_bay   \n",
       "2       496.0       177.0         7.2574            352100.0        near_bay   \n",
       "3       558.0       219.0         5.6431            341300.0        near_bay   \n",
       "4       565.0       259.0         3.8462            342200.0        near_bay   \n",
       "\n",
       "   rooms_per_household  bedrooms_per_room  population_per_household  \n",
       "0             6.984127           0.146591                  2.555556  \n",
       "1             6.238137           0.155797                  2.109842  \n",
       "2             8.288136           0.129516                  2.802260  \n",
       "3             5.817352           0.184458                  2.547945  \n",
       "4             6.281853           0.172096                  2.181467  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"rooms_per_household\"] = df[\"total_rooms\"]/df[\"households\"]\n",
    "df[\"bedrooms_per_room\"] = df[\"total_bedrooms\"]/df[\"total_rooms\"]\n",
    "df[\"population_per_household\"] = df[\"population\"]/df[\"households\"]\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7842e3a5",
   "metadata": {},
   "source": [
    "### Question 1\n",
    "\n",
    "What is the most frequent observation (mode) for the column `ocean_proximity`?\n",
    "\n",
    "Options:\n",
    "* `NEAR BAY`\n",
    "* `<1H OCEAN`\n",
    "* `INLAND`\n",
    "* `NEAR OCEAN`\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "a43defd0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    <1h_ocean\n",
       "Name: ocean_proximity, dtype: object"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.ocean_proximity.mode()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "736176a4",
   "metadata": {},
   "source": [
    "## Split the data\n",
    "\n",
    "* Split your data in train/val/test sets, with 60%/20%/20% distribution.\n",
    "* Use Scikit-Learn for that (the `train_test_split` function) and set the seed to 42.\n",
    "* Make sure that the target value (`median_house_value`) is not in your dataframe.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "991376ef",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>ocean_proximity</th>\n",
       "      <th>rooms_per_household</th>\n",
       "      <th>bedrooms_per_room</th>\n",
       "      <th>population_per_household</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-122.23</td>\n",
       "      <td>37.88</td>\n",
       "      <td>41.0</td>\n",
       "      <td>880.0</td>\n",
       "      <td>129.0</td>\n",
       "      <td>322.0</td>\n",
       "      <td>126.0</td>\n",
       "      <td>8.3252</td>\n",
       "      <td>near_bay</td>\n",
       "      <td>6.984127</td>\n",
       "      <td>0.146591</td>\n",
       "      <td>2.555556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-122.22</td>\n",
       "      <td>37.86</td>\n",
       "      <td>21.0</td>\n",
       "      <td>7099.0</td>\n",
       "      <td>1106.0</td>\n",
       "      <td>2401.0</td>\n",
       "      <td>1138.0</td>\n",
       "      <td>8.3014</td>\n",
       "      <td>near_bay</td>\n",
       "      <td>6.238137</td>\n",
       "      <td>0.155797</td>\n",
       "      <td>2.109842</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-122.24</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1467.0</td>\n",
       "      <td>190.0</td>\n",
       "      <td>496.0</td>\n",
       "      <td>177.0</td>\n",
       "      <td>7.2574</td>\n",
       "      <td>near_bay</td>\n",
       "      <td>8.288136</td>\n",
       "      <td>0.129516</td>\n",
       "      <td>2.802260</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1274.0</td>\n",
       "      <td>235.0</td>\n",
       "      <td>558.0</td>\n",
       "      <td>219.0</td>\n",
       "      <td>5.6431</td>\n",
       "      <td>near_bay</td>\n",
       "      <td>5.817352</td>\n",
       "      <td>0.184458</td>\n",
       "      <td>2.547945</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1627.0</td>\n",
       "      <td>280.0</td>\n",
       "      <td>565.0</td>\n",
       "      <td>259.0</td>\n",
       "      <td>3.8462</td>\n",
       "      <td>near_bay</td>\n",
       "      <td>6.281853</td>\n",
       "      <td>0.172096</td>\n",
       "      <td>2.181467</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
       "0    -122.23     37.88                41.0        880.0           129.0   \n",
       "1    -122.22     37.86                21.0       7099.0          1106.0   \n",
       "2    -122.24     37.85                52.0       1467.0           190.0   \n",
       "3    -122.25     37.85                52.0       1274.0           235.0   \n",
       "4    -122.25     37.85                52.0       1627.0           280.0   \n",
       "\n",
       "   population  households  median_income ocean_proximity  rooms_per_household  \\\n",
       "0       322.0       126.0         8.3252        near_bay             6.984127   \n",
       "1      2401.0      1138.0         8.3014        near_bay             6.238137   \n",
       "2       496.0       177.0         7.2574        near_bay             8.288136   \n",
       "3       558.0       219.0         5.6431        near_bay             5.817352   \n",
       "4       565.0       259.0         3.8462        near_bay             6.281853   \n",
       "\n",
       "   bedrooms_per_room  population_per_household  \n",
       "0           0.146591                  2.555556  \n",
       "1           0.155797                  2.109842  \n",
       "2           0.129516                  2.802260  \n",
       "3           0.184458                  2.547945  \n",
       "4           0.172096                  2.181467  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features = df.drop(columns = \"median_house_value\").copy()\n",
    "features.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "f793b648",
   "metadata": {},
   "outputs": [],
   "source": [
    "target = df[\"median_house_value\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "6a4ff0a7",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "x_train_full, x_test, y_train_full, y_test = train_test_split(features, target, test_size=0.2, random_state=42)\n",
    "x_train, x_val, y_train, y_val = train_test_split(x_train_full, y_train_full, test_size=0.25, random_state=42)\n",
    "\n",
    "splits = [x_train_full, x_train, x_val, x_test]\n",
    "for split in splits:\n",
    "        split.reset_index(inplace = True)\n",
    "        \n",
    "splits = [y_train_full,y_train, y_val, y_test]\n",
    "for split in splits:\n",
    "         split.reset_index(drop = True, inplace = True)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7983a474",
   "metadata": {},
   "source": [
    "### Question 2\n",
    "\n",
    "* Create the [correlation matrix](https://www.google.com/search?q=correlation+matrix) for the numerical features of your train dataset.\n",
    "    - In a correlation matrix, you compute the correlation coefficient between every pair of features in the dataset.\n",
    "* What are the two features that have the biggest correlation in this dataset?\n",
    "\n",
    "Options:\n",
    "* `total_bedrooms` and `households`\n",
    "* `total_bedrooms` and `total_rooms`\n",
    "* `population` and `households`\n",
    "* `population_per_household` and `total_rooms`\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "d7c2ee3f",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "numeric_columns = list(x_train.dtypes[x_train.dtypes != \"object\"].index)\n",
    "\n",
    "corr_matrix = x_train[numeric_columns].corr().abs()\n",
    "type(corr_matrix)\n",
    "# get upper triangular matrix becuase value will be reflected along the diagonal and return index of maximum values:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "e0478d95",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1500x1000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(15,10))\n",
    "ax = plt.gca()\n",
    "sns.heatmap(corr_matrix, ax=ax, annot=True)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "21290cd6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5, 7)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr_numpy  = corr_matrix.to_numpy()\n",
    "# np.shape(corr_numpy) # dont use len, size\n",
    "upper_indices = np.triu_indices(np.shape(corr_numpy)[0], 1)\n",
    "max_ind = np.argmax(corr_numpy[upper_indices])\n",
    "upper_indices[0][max_ind], upper_indices[1][max_ind]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "e1d25960",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'total_bedrooms'"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr_matrix.index[upper_indices[0][max_ind]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "fd2874cb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'households'"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr_matrix.columns[upper_indices[1][max_ind]]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fcdcfeaf",
   "metadata": {},
   "source": [
    "Total bedrooms and households"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "37c16b4a",
   "metadata": {},
   "source": [
    "### Make `median_house_value` binary\n",
    "\n",
    "* We need to turn the `median_house_value` variable from numeric into binary.\n",
    "* Let's create a variable `above_average` which is `1` if the `median_house_value` is above its mean value and `0` otherwise.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "0ebe5cdd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.series.Series"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "1b7f51e5",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_train_bin = y_train.copy()\n",
    "y_val_bin = y_val.copy()\n",
    "\n",
    "average= y_train_bin.mean()\n",
    "binarize = lambda x: 1 if x> average else 0\n",
    "\n",
    "y_train_bin = y_train_bin.apply(binarize)\n",
    "y_val_bin = y_val_bin.apply(binarize)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f38d4b63",
   "metadata": {},
   "source": [
    "### Question 3\n",
    "\n",
    "* Calculate the mutual information score with the (binarized) price for the categorical variable that we have. Use the training set only.\n",
    "* What is the value of mutual information?\n",
    "* Round it to 2 decimal digits using `round(score, 2)`\n",
    "\n",
    "Options:\n",
    "- 0.26\n",
    "- 0\n",
    "- 0.10\n",
    "- 0.16\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "ad1ce5a8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['ocean_proximity']"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "categorical_columns = list(x_train.dtypes[x_train.dtypes == \"object\"].index)\n",
    "categorical_columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "9a151698",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.1\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import mutual_info_score\n",
    "\n",
    "for column in categorical_columns:\n",
    "    print(round(mutual_info_score(x_train[column], y_train_bin),2))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6b895180",
   "metadata": {},
   "source": [
    "### Question 4\n",
    "\n",
    "* Now let's train a logistic regression\n",
    "* Remember that we have one categorical variable `ocean_proximity` in the data. Include it using one-hot encoding.\n",
    "* Fit the model on the training dataset.\n",
    "    - To make sure the results are reproducible across different versions of Scikit-Learn, fit the model with these parameters:\n",
    "    - `model = LogisticRegression(solver=\"liblinear\", C=1.0, max_iter=1000, random_state=42)`\n",
    "* Calculate the accuracy on the validation dataset and round it to 2 decimal digits.\n",
    "\n",
    "Options:\n",
    "- 0.60\n",
    "- 0.72\n",
    "- 0.84\n",
    "- 0.95\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a1bccdc8",
   "metadata": {},
   "source": [
    "One hot encoding of ocean_proximity:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "383bc987",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_extraction import DictVectorizer\n",
    "\n",
    "dv = DictVectorizer(sparse=False)\n",
    "\n",
    "train_dict = x_train.to_dict(orient='records')\n",
    "x_train_OH = dv.fit_transform(train_dict)\n",
    "\n",
    "val_dict = x_val.to_dict(orient='records')\n",
    "x_val_OH = dv.transform(val_dict)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "741a725a",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "model = LogisticRegression(solver=\"liblinear\", C=1.0, max_iter=1000, random_state=42)\n",
    "model.fit(x_train_OH, y_train_bin)\n",
    "y_pred = model.predict(x_val_OH)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "5b229acd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.83"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "original_features_accuracy = round(accuracy_score(y_val_bin, y_pred),2)\n",
    "original_features_accuracy"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e8b6a76c",
   "metadata": {},
   "source": [
    "### Question 5 \n",
    "\n",
    "* Let's find the least useful feature using the *feature elimination* technique.\n",
    "* Train a model with all these features (using the same parameters as in Q4).\n",
    "* Now exclude each feature from this set and train a model without it. Record the accuracy for each model.\n",
    "* For each feature, calculate the difference between the original accuracy and the accuracy without the feature. \n",
    "* Which of following feature has the smallest difference? \n",
    "   * `total_rooms`\n",
    "   * `total_bedrooms` \n",
    "   * `population`\n",
    "   * `households`\n",
    "\n",
    "> **note**: the difference doesn't have to be positive\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "1afbf64e",
   "metadata": {},
   "outputs": [],
   "source": [
    "column_names = x_train.columns\n",
    "accuracy_scores = []\n",
    "\n",
    "for column in column_names:\n",
    "    \n",
    "    x_train_dropped = x_train.drop(columns=column)\n",
    "    \n",
    "    train_dict = x_train_dropped.to_dict(orient='records')\n",
    "    x_train_dropped_OH = dv.fit_transform(train_dict)\n",
    "\n",
    "    val_dict = x_val.to_dict(orient='records')\n",
    "    x_val_dropped_OH = dv.transform(val_dict)\n",
    "    \n",
    "    model = LogisticRegression(solver=\"liblinear\", C=1.0, max_iter=1000, random_state=42)\n",
    "    model.fit(x_train_dropped_OH, y_train_bin)\n",
    "    y_pred = model.predict(x_val_dropped_OH)\n",
    "    \n",
    "    accuracy_scores.append(round(accuracy_score(y_val_bin, y_pred),2))\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "5033fb72",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy values are:  [0.83, 0.84, 0.83, 0.83, 0.84, 0.83, 0.84, 0.79, 0.82, 0.83, 0.83, 0.84]\n",
      "\n",
      "difference values are:  [0.010000000000000009, 0.0, 0.010000000000000009, 0.0, 0.0, 0.010000000000000009, 0.0, 0.010000000000000009, 0.039999999999999925, 0.010000000000000009, 0.0, 0.0, 0.010000000000000009]\n",
      "\n",
      "The feature whose absence creates the smallest difference is:  ['longitude', 'housing_median_age', 'total_rooms', 'population', 'rooms_per_household', 'bedrooms_per_room']\n"
     ]
    }
   ],
   "source": [
    "diff_accuracy = []\n",
    "for i in range(len(accuracy_scores)):    \n",
    "    diff_accuracy.append(original_features_accuracy - accuracy_scores[i])\n",
    "    \n",
    "diff_accuracy = list(map(abs, diff_accuracy))\n",
    "smallest_diff_accuracy = min(diff_accuracy)\n",
    "index_list = [index for index in range(len(diff_accuracy)) if diff_accuracy[index] == smallest_diff_accuracy]\n",
    "smallest_diff_feat = [column_names[index] for index in index_list]\n",
    "\n",
    "print(\"Accuracy values are: \", accuracy_scores[1:] )\n",
    "print(\"\\ndifference values are: \", diff_accuracy)\n",
    "print (\"\\nThe feature whose absence creates the smallest difference is: \", smallest_diff_feat)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "267a8e58",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['total_rooms', 'population']"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "question_feat = ['total_rooms', 'total_bedrooms', 'population', 'households']\n",
    "[feat for feat in smallest_diff_feat if feat in question_feat]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b113ee7f",
   "metadata": {},
   "source": [
    "### Question 6\n",
    "\n",
    "* For this question, we'll see how to use a linear regression model from Scikit-Learn\n",
    "* We'll need to use the original column `'median_house_value'`. Apply the logarithmic transformation to this column.\n",
    "* Fit the Ridge regression model (`model = Ridge(alpha=a, solver=\"sag\", random_state=42)`) on the training data.\n",
    "* This model has a parameter `alpha`. Let's try the following values: `[0, 0.01, 0.1, 1, 10]`\n",
    "* Which of these alphas leads to the best RMSE on the validation set? Round your RMSE scores to 3 decimal digits.\n",
    "\n",
    "If there are multiple options, select the smallest `alpha`.\n",
    "\n",
    "Options:\n",
    "- 0\n",
    "- 0.01\n",
    "- 0.1\n",
    "- 1\n",
    "- 10\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "ca978cf8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    13.022766\n",
       "1    12.789687\n",
       "2    12.771673\n",
       "3    12.740520\n",
       "4    12.743154\n",
       "Name: median_house_value, dtype: float64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "log_target = np.log1p(target)\n",
    "log_target.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "edccac12",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train_full, x_test, y_train_full, y_test = train_test_split(features, log_target, test_size=0.2, random_state=42)\n",
    "x_train, x_val, y_train, y_val = train_test_split(x_train_full, y_train_full, test_size=0.25, random_state=42)\n",
    "\n",
    "splits = [x_train_full, x_train, x_val, x_test]\n",
    "for split in splits:\n",
    "        split.reset_index(inplace = True)\n",
    "        \n",
    "splits = [y_train_full,y_train, y_val, y_test]\n",
    "for split in splits:\n",
    "         split.reset_index(drop = True, inplace = True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "0652a4c8",
   "metadata": {},
   "outputs": [],
   "source": [
    "def calculate_rmse(y_test, y_pred):\n",
    "    error = y_pred - y_test\n",
    "    mse = (error ** 2).mean()\n",
    "    rmse =np.sqrt(mse)\n",
    "    return rmse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "d7a248b5",
   "metadata": {},
   "outputs": [],
   "source": [
    "dv = DictVectorizer(sparse=False)\n",
    "\n",
    "train_dict = x_train_full.to_dict(orient='records')\n",
    "x_train_full_OH = dv.fit_transform(train_dict)\n",
    "\n",
    "test_dict = x_test.to_dict(orient='records')\n",
    "x_test_OH = dv.transform(test_dict)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "0c4b4f6b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RSME values are:  [0.564, 0.564, 0.564, 0.564, 0.564]\n",
      "Best alpha values are:  [0, 0.01, 0.1, 1, 10]\n",
      "The smallest best alpha value is:  0\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import Ridge\n",
    "\n",
    "alpha_values = [0, 0.01, 0.1, 1, 10]\n",
    "RMSE = []\n",
    "\n",
    "for alpha in alpha_values:\n",
    "    model = Ridge(alpha=alpha, solver=\"sag\", random_state=42)\n",
    "    model.fit(x_train_full_OH, y_train_full)\n",
    "    y_pred = model.predict(x_test_OH)\n",
    "     \n",
    "    rmse = calculate_rmse(y_test, y_pred)     \n",
    "    RMSE.append(round(rmse,3))\n",
    "    \n",
    "best_RMSE = min(RMSE)\n",
    "index_list = [index for index in range(len(RMSE)) if RMSE[index] == best_RMSE]\n",
    "best_alpha_values = [alpha_values[index] for index in index_list]\n",
    "\n",
    "print(\"RSME values are: \", RMSE)\n",
    "print(\"Best alpha values are: \", best_alpha_values)\n",
    "print (\"The smallest best alpha value is: \", min(best_alpha_values))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a2a86c14",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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