HW_Regression_SamuelAdi.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "efd09515",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd \n",
    "import matplotlib.pyplot as plt \n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cfaede3d",
   "metadata": {},
   "source": [
    "### 1. Load The Data "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "88f20fc1",
   "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>crim</th>\n",
       "      <th>zn</th>\n",
       "      <th>indus</th>\n",
       "      <th>chas</th>\n",
       "      <th>nox</th>\n",
       "      <th>rm</th>\n",
       "      <th>age</th>\n",
       "      <th>dis</th>\n",
       "      <th>rad</th>\n",
       "      <th>tax</th>\n",
       "      <th>ptratio</th>\n",
       "      <th>black</th>\n",
       "      <th>lstat</th>\n",
       "      <th>medv</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.00632</td>\n",
       "      <td>18.0</td>\n",
       "      <td>2.31</td>\n",
       "      <td>0</td>\n",
       "      <td>0.538</td>\n",
       "      <td>6.575</td>\n",
       "      <td>65.2</td>\n",
       "      <td>4.0900</td>\n",
       "      <td>1</td>\n",
       "      <td>296</td>\n",
       "      <td>15.3</td>\n",
       "      <td>396.90</td>\n",
       "      <td>4.98</td>\n",
       "      <td>24.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.02731</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.07</td>\n",
       "      <td>0</td>\n",
       "      <td>0.469</td>\n",
       "      <td>6.421</td>\n",
       "      <td>78.9</td>\n",
       "      <td>4.9671</td>\n",
       "      <td>2</td>\n",
       "      <td>242</td>\n",
       "      <td>17.8</td>\n",
       "      <td>396.90</td>\n",
       "      <td>9.14</td>\n",
       "      <td>21.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.02729</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.07</td>\n",
       "      <td>0</td>\n",
       "      <td>0.469</td>\n",
       "      <td>7.185</td>\n",
       "      <td>61.1</td>\n",
       "      <td>4.9671</td>\n",
       "      <td>2</td>\n",
       "      <td>242</td>\n",
       "      <td>17.8</td>\n",
       "      <td>392.83</td>\n",
       "      <td>4.03</td>\n",
       "      <td>34.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.03237</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.18</td>\n",
       "      <td>0</td>\n",
       "      <td>0.458</td>\n",
       "      <td>6.998</td>\n",
       "      <td>45.8</td>\n",
       "      <td>6.0622</td>\n",
       "      <td>3</td>\n",
       "      <td>222</td>\n",
       "      <td>18.7</td>\n",
       "      <td>394.63</td>\n",
       "      <td>2.94</td>\n",
       "      <td>33.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.06905</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.18</td>\n",
       "      <td>0</td>\n",
       "      <td>0.458</td>\n",
       "      <td>7.147</td>\n",
       "      <td>54.2</td>\n",
       "      <td>6.0622</td>\n",
       "      <td>3</td>\n",
       "      <td>222</td>\n",
       "      <td>18.7</td>\n",
       "      <td>396.90</td>\n",
       "      <td>5.33</td>\n",
       "      <td>36.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>501</th>\n",
       "      <td>0.06263</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.93</td>\n",
       "      <td>0</td>\n",
       "      <td>0.573</td>\n",
       "      <td>6.593</td>\n",
       "      <td>69.1</td>\n",
       "      <td>2.4786</td>\n",
       "      <td>1</td>\n",
       "      <td>273</td>\n",
       "      <td>21.0</td>\n",
       "      <td>391.99</td>\n",
       "      <td>9.67</td>\n",
       "      <td>22.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>502</th>\n",
       "      <td>0.04527</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.93</td>\n",
       "      <td>0</td>\n",
       "      <td>0.573</td>\n",
       "      <td>6.120</td>\n",
       "      <td>76.7</td>\n",
       "      <td>2.2875</td>\n",
       "      <td>1</td>\n",
       "      <td>273</td>\n",
       "      <td>21.0</td>\n",
       "      <td>396.90</td>\n",
       "      <td>9.08</td>\n",
       "      <td>20.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>503</th>\n",
       "      <td>0.06076</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.93</td>\n",
       "      <td>0</td>\n",
       "      <td>0.573</td>\n",
       "      <td>6.976</td>\n",
       "      <td>91.0</td>\n",
       "      <td>2.1675</td>\n",
       "      <td>1</td>\n",
       "      <td>273</td>\n",
       "      <td>21.0</td>\n",
       "      <td>396.90</td>\n",
       "      <td>5.64</td>\n",
       "      <td>23.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>504</th>\n",
       "      <td>0.10959</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.93</td>\n",
       "      <td>0</td>\n",
       "      <td>0.573</td>\n",
       "      <td>6.794</td>\n",
       "      <td>89.3</td>\n",
       "      <td>2.3889</td>\n",
       "      <td>1</td>\n",
       "      <td>273</td>\n",
       "      <td>21.0</td>\n",
       "      <td>393.45</td>\n",
       "      <td>6.48</td>\n",
       "      <td>22.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>505</th>\n",
       "      <td>0.04741</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.93</td>\n",
       "      <td>0</td>\n",
       "      <td>0.573</td>\n",
       "      <td>6.030</td>\n",
       "      <td>80.8</td>\n",
       "      <td>2.5050</td>\n",
       "      <td>1</td>\n",
       "      <td>273</td>\n",
       "      <td>21.0</td>\n",
       "      <td>396.90</td>\n",
       "      <td>7.88</td>\n",
       "      <td>11.9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>506 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        crim    zn  indus  chas    nox     rm   age     dis  rad  tax  \\\n",
       "0    0.00632  18.0   2.31     0  0.538  6.575  65.2  4.0900    1  296   \n",
       "1    0.02731   0.0   7.07     0  0.469  6.421  78.9  4.9671    2  242   \n",
       "2    0.02729   0.0   7.07     0  0.469  7.185  61.1  4.9671    2  242   \n",
       "3    0.03237   0.0   2.18     0  0.458  6.998  45.8  6.0622    3  222   \n",
       "4    0.06905   0.0   2.18     0  0.458  7.147  54.2  6.0622    3  222   \n",
       "..       ...   ...    ...   ...    ...    ...   ...     ...  ...  ...   \n",
       "501  0.06263   0.0  11.93     0  0.573  6.593  69.1  2.4786    1  273   \n",
       "502  0.04527   0.0  11.93     0  0.573  6.120  76.7  2.2875    1  273   \n",
       "503  0.06076   0.0  11.93     0  0.573  6.976  91.0  2.1675    1  273   \n",
       "504  0.10959   0.0  11.93     0  0.573  6.794  89.3  2.3889    1  273   \n",
       "505  0.04741   0.0  11.93     0  0.573  6.030  80.8  2.5050    1  273   \n",
       "\n",
       "     ptratio   black  lstat  medv  \n",
       "0       15.3  396.90   4.98  24.0  \n",
       "1       17.8  396.90   9.14  21.6  \n",
       "2       17.8  392.83   4.03  34.7  \n",
       "3       18.7  394.63   2.94  33.4  \n",
       "4       18.7  396.90   5.33  36.2  \n",
       "..       ...     ...    ...   ...  \n",
       "501     21.0  391.99   9.67  22.4  \n",
       "502     21.0  396.90   9.08  20.6  \n",
       "503     21.0  396.90   5.64  23.9  \n",
       "504     21.0  393.45   6.48  22.0  \n",
       "505     21.0  396.90   7.88  11.9  \n",
       "\n",
       "[506 rows x 14 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv('boston.csv')\n",
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f3362bf6",
   "metadata": {},
   "source": [
    "### 2. Split Data (Train, Validation, Test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "555fb997",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Defining the target and feature, target = medv\n",
    "feature = data.drop(columns='medv')\n",
    "target = data['medv']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "a75d9709",
   "metadata": {},
   "outputs": [],
   "source": [
    "# split the data into train, validate, test \n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "feature_pretrain_data, feature_test_data, target_pretrain_data, target_test_data = train_test_split(feature, target, test_size= 0.2, random_state= 53)\n",
    "feature_train_data, feature_validate_data, target_train_data, target_validate_data = train_test_split(feature_pretrain_data, target_pretrain_data, test_size= 0.2, random_state= 52)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "07fc1a1d",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ASUS\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\tsatools.py:142: FutureWarning: In a future version of pandas all arguments of concat except for the argument 'objs' will be keyword-only\n",
      "  x = pd.concat(x[::order], 1)\n"
     ]
    },
    {
     "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>feature</th>\n",
       "      <th>vif_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>crim</td>\n",
       "      <td>1.788472</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>zn</td>\n",
       "      <td>2.467090</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>indus</td>\n",
       "      <td>4.722670</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>chas</td>\n",
       "      <td>1.091597</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>nox</td>\n",
       "      <td>4.726355</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>rm</td>\n",
       "      <td>1.866751</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>age</td>\n",
       "      <td>3.200367</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>dis</td>\n",
       "      <td>4.033914</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>rad</td>\n",
       "      <td>7.032778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>tax</td>\n",
       "      <td>8.914045</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>ptratio</td>\n",
       "      <td>1.826174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>black</td>\n",
       "      <td>1.350360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>lstat</td>\n",
       "      <td>3.055763</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    feature  vif_score\n",
       "1      crim   1.788472\n",
       "2        zn   2.467090\n",
       "3     indus   4.722670\n",
       "4      chas   1.091597\n",
       "5       nox   4.726355\n",
       "6        rm   1.866751\n",
       "7       age   3.200367\n",
       "8       dis   4.033914\n",
       "9       rad   7.032778\n",
       "10      tax   8.914045\n",
       "11  ptratio   1.826174\n",
       "12    black   1.350360\n",
       "13    lstat   3.055763"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# calculate vif score \n",
    "from statsmodels.stats.outliers_influence import variance_inflation_factor as vif \n",
    "from statsmodels.tools.tools import add_constant\n",
    "\n",
    "x = add_constant(feature_train_data)\n",
    "vif_df = pd.DataFrame([vif(x.values, i) for i in range(x.shape[1])], index=x.columns).reset_index()\n",
    "vif_df.columns = ['feature','vif_score']\n",
    "vif_df = vif_df.loc[vif_df.feature!='const']\n",
    "vif_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d7df572d",
   "metadata": {},
   "source": [
    "Feature that Highly correlated (VIF > 5) : \n",
    "1. tax\n",
    "2. rad"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e4c99fca",
   "metadata": {},
   "source": [
    "### 3. Correlation Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "9dda7a90",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# analyse the training data \n",
    "data_train = pd.concat([feature_train_data, target_train_data], axis = 1)\n",
    "train_corr = data_train.corr()\n",
    "plt.figure(figsize = (12,10))\n",
    "sns.heatmap(train_corr, annot=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "acb0a520",
   "metadata": {},
   "source": [
    "Based on this heatmap, i want to retain rad and drop tax from feature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "3337008b",
   "metadata": {},
   "outputs": [],
   "source": [
    "feature_train_data = feature_train_data.drop(columns= 'tax')\n",
    "feature_test_data = feature_test_data.drop(columns= 'tax')\n",
    "feature_validate_data = feature_validate_data.drop(columns= 'tax')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "819aad36",
   "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>crim</th>\n",
       "      <th>zn</th>\n",
       "      <th>indus</th>\n",
       "      <th>chas</th>\n",
       "      <th>nox</th>\n",
       "      <th>rm</th>\n",
       "      <th>age</th>\n",
       "      <th>dis</th>\n",
       "      <th>rad</th>\n",
       "      <th>ptratio</th>\n",
       "      <th>black</th>\n",
       "      <th>lstat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>290</th>\n",
       "      <td>0.03502</td>\n",
       "      <td>80.0</td>\n",
       "      <td>4.95</td>\n",
       "      <td>0</td>\n",
       "      <td>0.411</td>\n",
       "      <td>6.861</td>\n",
       "      <td>27.9</td>\n",
       "      <td>5.1167</td>\n",
       "      <td>4</td>\n",
       "      <td>19.2</td>\n",
       "      <td>396.90</td>\n",
       "      <td>3.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>0.05789</td>\n",
       "      <td>12.5</td>\n",
       "      <td>6.07</td>\n",
       "      <td>0</td>\n",
       "      <td>0.409</td>\n",
       "      <td>5.878</td>\n",
       "      <td>21.4</td>\n",
       "      <td>6.4980</td>\n",
       "      <td>4</td>\n",
       "      <td>18.9</td>\n",
       "      <td>396.21</td>\n",
       "      <td>8.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>272</th>\n",
       "      <td>0.11460</td>\n",
       "      <td>20.0</td>\n",
       "      <td>6.96</td>\n",
       "      <td>0</td>\n",
       "      <td>0.464</td>\n",
       "      <td>6.538</td>\n",
       "      <td>58.7</td>\n",
       "      <td>3.9175</td>\n",
       "      <td>3</td>\n",
       "      <td>18.6</td>\n",
       "      <td>394.96</td>\n",
       "      <td>7.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>1.38799</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.14</td>\n",
       "      <td>0</td>\n",
       "      <td>0.538</td>\n",
       "      <td>5.950</td>\n",
       "      <td>82.0</td>\n",
       "      <td>3.9900</td>\n",
       "      <td>4</td>\n",
       "      <td>21.0</td>\n",
       "      <td>232.60</td>\n",
       "      <td>27.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>189</th>\n",
       "      <td>0.08370</td>\n",
       "      <td>45.0</td>\n",
       "      <td>3.44</td>\n",
       "      <td>0</td>\n",
       "      <td>0.437</td>\n",
       "      <td>7.185</td>\n",
       "      <td>38.9</td>\n",
       "      <td>4.5667</td>\n",
       "      <td>5</td>\n",
       "      <td>15.2</td>\n",
       "      <td>396.90</td>\n",
       "      <td>5.39</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        crim    zn  indus  chas    nox     rm   age     dis  rad  ptratio  \\\n",
       "290  0.03502  80.0   4.95     0  0.411  6.861  27.9  5.1167    4     19.2   \n",
       "67   0.05789  12.5   6.07     0  0.409  5.878  21.4  6.4980    4     18.9   \n",
       "272  0.11460  20.0   6.96     0  0.464  6.538  58.7  3.9175    3     18.6   \n",
       "32   1.38799   0.0   8.14     0  0.538  5.950  82.0  3.9900    4     21.0   \n",
       "189  0.08370  45.0   3.44     0  0.437  7.185  38.9  4.5667    5     15.2   \n",
       "\n",
       "      black  lstat  \n",
       "290  396.90   3.33  \n",
       "67   396.21   8.10  \n",
       "272  394.96   7.73  \n",
       "32   232.60  27.71  \n",
       "189  396.90   5.39  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# recheck the data\n",
    "feature_train_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "241259e4",
   "metadata": {},
   "source": [
    "### 4. Fit Data on The Train Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "706fec6f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# lambda = [0.01, 0.1, 1, 10] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "f152cf51",
   "metadata": {},
   "outputs": [],
   "source": [
    "# defining x and y data train\n",
    "x_data_train = feature_train_data.to_numpy()\n",
    "y_data_train = target_train_data.to_numpy()\n",
    "y_data_train = y_data_train.reshape(len(y_data_train),)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "167967f8",
   "metadata": {},
   "source": [
    "#### A. Ridge"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "14dbc530",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ridge(alpha=10, random_state=52)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import Ridge\n",
    "# Define the model \n",
    "ridge_reg_pointzeroone = Ridge(alpha = 0.01, random_state=52)\n",
    "ridge_reg_pointone = Ridge(alpha = 0.1, random_state=52)\n",
    "ridge_reg_one = Ridge(alpha = 1, random_state=52)\n",
    "ridge_reg_ten = Ridge(alpha=10, random_state=52)\n",
    "\n",
    "# fit the model \n",
    "ridge_reg_pointzeroone.fit(x_data_train, y_data_train)\n",
    "ridge_reg_pointone.fit(x_data_train, y_data_train)\n",
    "ridge_reg_one.fit(x_data_train, y_data_train)\n",
    "ridge_reg_ten.fit(x_data_train, y_data_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fc85c71f",
   "metadata": {},
   "source": [
    "#### B. LASSO"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "03288604",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Lasso(alpha=10, random_state=52)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import Lasso\n",
    "# Define the model \n",
    "lasso_reg_pointzeroone = Lasso(alpha=0.01, random_state=52)\n",
    "lasso_reg_pointone = Lasso(alpha=0.1, random_state=52)\n",
    "lasso_reg_one = Lasso(alpha=1, random_state=52)\n",
    "lasso_reg_ten = Lasso(alpha=10, random_state=52)\n",
    "\n",
    "#fit the model \n",
    "lasso_reg_pointzeroone.fit(x_data_train, y_data_train)\n",
    "lasso_reg_pointone.fit(x_data_train, y_data_train)\n",
    "lasso_reg_one.fit(x_data_train, y_data_train)\n",
    "lasso_reg_ten.fit(x_data_train, y_data_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0546460c",
   "metadata": {},
   "source": [
    "### 5. Choose The Best Lambda"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "7aca327a",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import mean_squared_error\n",
    "alphas = [0.01, 0.1, 1, 10]\n",
    "\n",
    "# defining x and y data validation \n",
    "x_data_validation = feature_validate_data.to_numpy()\n",
    "y_data_validation = target_validate_data.to_numpy()\n",
    "y_data_validation = y_data_validation.reshape(len(y_data_validation),)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fee2a8a9",
   "metadata": {},
   "source": [
    "#### A. Ridge "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "16c2f61c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# modelling based on validation data\n",
    "models_ridge = [ridge_reg_pointzeroone, ridge_reg_pointone,\n",
    "              ridge_reg_one, ridge_reg_ten]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "179f3c6d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE of Ridge model with alpha = 0.01  is  3.8187230938414913\n",
      "RMSE of Ridge model with alpha = 0.1  is  3.8139513990085914\n",
      "RMSE of Ridge model with alpha = 1  is  3.804728971631597\n",
      "RMSE of Ridge model with alpha = 10  is  3.8548006201947467\n"
     ]
    }
   ],
   "source": [
    "for model_ridge, alpha in zip(models_ridge,alphas):\n",
    "    y_medv_validation_ridge = model_ridge.predict(x_data_validation)\n",
    "    rmse_ridge = np.sqrt(mean_squared_error(y_data_validation,y_medv_validation_ridge))\n",
    "    print('RMSE of Ridge model with alpha =', alpha,' is ',rmse_ridge)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "42aac85d",
   "metadata": {},
   "source": [
    "#### B. LASSO"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "3e177bc4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# modelling based on validation data \n",
    "models_lasso = [lasso_reg_pointzeroone, lasso_reg_pointone,\n",
    "               lasso_reg_one, lasso_reg_ten]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "83041072",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE of LASSO model with alpha = 0.01  is  3.8069347793672295\n",
      "RMSE of LASSO model with alpha = 0.1  is  3.831339862361522\n",
      "RMSE of LASSO model with alpha = 1  is  4.455651227470317\n",
      "RMSE of LASSO model with alpha = 10  is  5.904126108704242\n"
     ]
    }
   ],
   "source": [
    "for model_lasso, alpha in zip(models_lasso,alphas):\n",
    "    y_medv_validation_lasso = model_lasso.predict(x_data_validation)\n",
    "    rmse_lasso = np.sqrt(mean_squared_error(y_data_validation,y_medv_validation_lasso))\n",
    "    print('RMSE of LASSO model with alpha =', alpha,' is ',rmse_lasso)\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "152fc834",
   "metadata": {},
   "source": [
    "Ridge model with alpha = 1 is the best model "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "c60b1b8f",
   "metadata": {},
   "outputs": [],
   "source": [
    "best_model = ridge_reg_one"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eb752104",
   "metadata": {},
   "source": [
    "#### C. Model Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "00069858",
   "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>feature</th>\n",
       "      <th>coefficient</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>intercept</td>\n",
       "      <td>25.753573</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>crim</td>\n",
       "      <td>-0.130035</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>zn</td>\n",
       "      <td>0.039004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>indus</td>\n",
       "      <td>-0.145408</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>chas</td>\n",
       "      <td>1.990875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>nox</td>\n",
       "      <td>-6.712162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>rm</td>\n",
       "      <td>4.030237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>age</td>\n",
       "      <td>-0.008529</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>dis</td>\n",
       "      <td>-1.318559</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>rad</td>\n",
       "      <td>0.101550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>ptratio</td>\n",
       "      <td>-0.824004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>black</td>\n",
       "      <td>0.009725</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>lstat</td>\n",
       "      <td>-0.569894</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      feature  coefficient\n",
       "0   intercept    25.753573\n",
       "1        crim    -0.130035\n",
       "2          zn     0.039004\n",
       "3       indus    -0.145408\n",
       "4        chas     1.990875\n",
       "5         nox    -6.712162\n",
       "6          rm     4.030237\n",
       "7         age    -0.008529\n",
       "8         dis    -1.318559\n",
       "9         rad     0.101550\n",
       "10    ptratio    -0.824004\n",
       "11      black     0.009725\n",
       "12      lstat    -0.569894"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#create coefficient dataframe\n",
    "coef_df = pd.DataFrame({\n",
    "     'feature':['intercept'] + feature_train_data.columns.tolist(),\n",
    "    'coefficient':[best_model.intercept_] + list(best_model.coef_)\n",
    "})\n",
    "coef_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6ab409b1",
   "metadata": {},
   "source": [
    "Diagnostic Study "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "1619ee24",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R-squared training data: 0.7617022653846209\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import r2_score\n",
    "\n",
    "y_predict_train = best_model.predict(x_data_train)\n",
    "print('R-squared training data: {}'.format(r2_score(y_data_train, y_predict_train)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "ec32dfe5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.lines.Line2D at 0x22c3fd1e040>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# define residual \n",
    "residual = y_data_train - y_predict_train\n",
    "#create residual dataframe \n",
    "df_resid = pd.DataFrame({\n",
    "     'predicted_value': y_predict_train,\n",
    "    'residual': residual\n",
    "})\n",
    "# plotting\n",
    "plt.figure(figsize=(8,5))\n",
    "sns.scatterplot(x = df_resid['predicted_value'], y = df_resid['residual'], alpha = 0.7)\n",
    "plt.axhline(0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "65dd5bfe",
   "metadata": {},
   "source": [
    "### 6. Training Error "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "8580000b",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_train_predict = best_model.predict(x_data_train)\n",
    "\n",
    "from sklearn.metrics import mean_absolute_error\n",
    "from sklearn.metrics import mean_absolute_percentage_error\n",
    "from sklearn.metrics import mean_squared_error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "2f91c591",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MAE for training data is 3.151174238535841\n",
      "MAPE for training data is 0.15740860489545824\n",
      "RMSE for training data is 4.43251368644229\n"
     ]
    }
   ],
   "source": [
    "print('MAE for training data is {}'.format(mean_absolute_error(y_data_train,y_train_predict)))\n",
    "print('MAPE for training data is {}'.format(mean_absolute_percentage_error(y_data_train,y_train_predict)))\n",
    "print('RMSE for training data is {}'.format(np.sqrt(mean_squared_error(y_data_train,y_train_predict))))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "00106416",
   "metadata": {},
   "source": [
    "### 7. Testing Error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "5cc0da0f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# defining variables for testing (using test data)\n",
    "x_data_test = feature_test_data.to_numpy()\n",
    "y_test_predict = best_model.predict(x_data_test)\n",
    "\n",
    "# Ground Truth target\n",
    "y_data_test = target_test_data.to_numpy()\n",
    "y_data_test = y_data_test.reshape(len(y_data_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "9fa0c532",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MAE for testing data is 4.098676878037026\n",
      "MAPE for testing data is 0.20842860015820255\n",
      "RMSE for testing data is 6.390491254385666\n"
     ]
    }
   ],
   "source": [
    "print('MAE for testing data is {}'.format(mean_absolute_error(y_data_test,y_test_predict)))\n",
    "print('MAPE for testing data is {}'.format(mean_absolute_percentage_error(y_data_test,y_test_predict)))\n",
    "print('RMSE for testing data is {}'.format(np.sqrt(mean_squared_error(y_data_test,y_test_predict))))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "261da78c",
   "metadata": {},
   "source": [
    "The best model for this dataset is a ridge using MAE(mean absolute error)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "42b876bc",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}