Regression Models.ipynb

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  {
   "cell_type": "markdown",
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   "source": [
    "## Understanding Regression in Machine Learning\n",
    "\n",
    "Regression in machine learning is a type of predictive modeling technique. It investigates the relationship between a dependent (target) variable and one or more independent (predictor) variables. The goal is to model the underlying relationship between these variables so that, given new data for the independent variables, we can accurately predict the dependent variable. Let's briefly overview the concept and its application in machine learning without diving deep into specific models:\n",
    "\n",
    "### Purpose of Regression\n",
    "\n",
    "The primary purpose of regression is to predict continuous values. For instance, predicting house prices, temperature, sales amounts, and more, based on historical data and the relationships identified between variables.\n",
    "\n",
    "### How It Works\n",
    "\n",
    "Regression works by fitting a line or curve (depending on the model) to the data points in a way that makes the distance from each data point to the line (known as the error) as small as possible. This line or curve represents the predicted relationship between the independent and dependent variables.\n",
    "\n",
    "### Types of Regression\n",
    "\n",
    "There are several types of regression models, each with its own mathematical approach to modeling the data:\n",
    "\n",
    "- **Linear Regression:** Assumes a linear relationship between the dependent and independent variables. It fits a straight line to the data.\n",
    "- **Polynomial Regression:** Extends linear regression to model non-linear relationships by using higher-degree polynomials.\n",
    "- **Ridge and Lasso Regression:** These are variations of linear regression that incorporate regularization to prevent overfitting by penalizing large coefficients.\n",
    "- **ElasticNet Regression:** Combines features of both Ridge and Lasso regression, using a mix of L1 and L2 regularization.\n",
    "- **Support Vector Regression (SVR):** Adapts the Support Vector Machine algorithm for regression, focusing on fitting the error within a certain threshold.\n",
    "- **Decision Tree Regression:** Uses a decision tree to model non-linear relationships by splitting the data into subsets based on the feature values.\n",
    "- **Gradient Boosting Regression:** An ensemble technique that builds multiple models sequentially, each new model correcting errors made by the previous ones.\n",
    "\n",
    "### Key Considerations\n",
    "\n",
    "- **Model Selection:** Choosing the right model depends on the nature of the data and the relationship between variables. Some datasets are best modeled with a simple linear approach, while others require more complex non-linear models.\n",
    "- **Overfitting vs. Underfitting:** A key challenge in regression is balancing model complexity with predictive accuracy. Overfitting occurs when the model is too complex, capturing noise instead of the underlying pattern. Underfitting happens when the model is too simple to capture the relationship between variables.\n",
    "- **Evaluation Metrics:** Metrics like MAE, MSE, RMSE, and R^2 score are used to evaluate the performance of regression models, providing insights into their accuracy and predictive capabilities.\n",
    "\n",
    "In machine learning, regression is a fundamental technique for predictive analysis, offering a wide range of models to fit the diverse nature of data and prediction tasks.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "252d1725",
   "metadata": {},
   "source": [
    "# Boston Housing Dataset Overview\n",
    "\n",
    "The Boston Housing dataset contains information about various towns and suburbs in the Boston Standard Metropolitan Statistical Area (SMSA) as of 1970. This dataset is widely used in machine learning for regression analysis and predictive modeling. The data includes several attributes for each area, sourced from the UCI Machine Learning Repository.\n",
    "\n",
    "## Attributes:\n",
    "\n",
    "- `CRIM`: Per capita crime rate by town\n",
    "- `ZN`: Proportion of residential land zoned for lots over 25,000 sq.ft.\n",
    "- `INDUS`: Proportion of non-retail business acres per town\n",
    "- `CHAS`: Charles River dummy variable (1 if tract bounds river; 0 otherwise)\n",
    "- `NOX`: Nitric oxides concentration (parts per 10 million)\n",
    "- `RM`: Average number of rooms per dwelling\n",
    "- `AGE`: Proportion of owner-occupied units built prior to 1940\n",
    "- `DIS`: Weighted distances to five Boston employment centers\n",
    "- `RAD`: Index of accessibility to radial highways\n",
    "- `TAX`: Full-value property-tax rate per \\$10,000\n",
    "- `PTRATIO`: Pupil-teacher ratio by town\n",
    "- `B`: 1000(Bk - 0.63)^2 where Bk is the proportion of blacks by town\n",
    "- `LSTAT`: % lower status of the population\n",
    "- `MEDV`: Median value of owner-occupied homes in $1000s\n",
    "\n",
    "For more detailed information and data download, visit the [UCI Machine Learning Repository Housing Dataset page](https://archive.ics.uci.edu/ml/datasets/Housing)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "6f66181b",
   "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": "ac1836ee",
   "metadata": {},
   "source": [
    "## Loading data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "19809cf2",
   "metadata": {},
   "outputs": [],
   "source": [
    "column_names = [\n",
    "    'CRIM', 'ZN', 'INDUS', 'CHAS', 'NOX', 'RM', 'AGE', 'DIS', 'RAD', 'TAX',\n",
    "    'PTRATIO', 'B', 'LSTAT', 'MEDV'\n",
    "]\n",
    "\n",
    "df = pd.read_csv('./data/housing.csv',\n",
    "                 header=None,\n",
    "                 delimiter=r\"\\s+\",\n",
    "                 names=column_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "054caa2f",
   "metadata": {},
   "outputs": [
    {
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       "  <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>B</th>\n",
       "      <th>LSTAT</th>\n",
       "      <th>MEDV</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>198</th>\n",
       "      <td>0.03768</td>\n",
       "      <td>80.0</td>\n",
       "      <td>1.52</td>\n",
       "      <td>0</td>\n",
       "      <td>0.404</td>\n",
       "      <td>7.274</td>\n",
       "      <td>38.3</td>\n",
       "      <td>7.3090</td>\n",
       "      <td>2</td>\n",
       "      <td>329.0</td>\n",
       "      <td>12.6</td>\n",
       "      <td>392.20</td>\n",
       "      <td>6.62</td>\n",
       "      <td>34.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>317</th>\n",
       "      <td>0.24522</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.90</td>\n",
       "      <td>0</td>\n",
       "      <td>0.544</td>\n",
       "      <td>5.782</td>\n",
       "      <td>71.7</td>\n",
       "      <td>4.0317</td>\n",
       "      <td>4</td>\n",
       "      <td>304.0</td>\n",
       "      <td>18.4</td>\n",
       "      <td>396.90</td>\n",
       "      <td>15.94</td>\n",
       "      <td>19.8</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>273.0</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>456</th>\n",
       "      <td>4.66883</td>\n",
       "      <td>0.0</td>\n",
       "      <td>18.10</td>\n",
       "      <td>0</td>\n",
       "      <td>0.713</td>\n",
       "      <td>5.976</td>\n",
       "      <td>87.9</td>\n",
       "      <td>2.5806</td>\n",
       "      <td>24</td>\n",
       "      <td>666.0</td>\n",
       "      <td>20.2</td>\n",
       "      <td>10.48</td>\n",
       "      <td>19.01</td>\n",
       "      <td>12.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>457</th>\n",
       "      <td>8.20058</td>\n",
       "      <td>0.0</td>\n",
       "      <td>18.10</td>\n",
       "      <td>0</td>\n",
       "      <td>0.713</td>\n",
       "      <td>5.936</td>\n",
       "      <td>80.3</td>\n",
       "      <td>2.7792</td>\n",
       "      <td>24</td>\n",
       "      <td>666.0</td>\n",
       "      <td>20.2</td>\n",
       "      <td>3.50</td>\n",
       "      <td>16.94</td>\n",
       "      <td>13.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      "text/plain": [
       "        CRIM    ZN  INDUS  CHAS    NOX     RM   AGE     DIS  RAD    TAX  \\\n",
       "198  0.03768  80.0   1.52     0  0.404  7.274  38.3  7.3090    2  329.0   \n",
       "317  0.24522   0.0   9.90     0  0.544  5.782  71.7  4.0317    4  304.0   \n",
       "502  0.04527   0.0  11.93     0  0.573  6.120  76.7  2.2875    1  273.0   \n",
       "456  4.66883   0.0  18.10     0  0.713  5.976  87.9  2.5806   24  666.0   \n",
       "457  8.20058   0.0  18.10     0  0.713  5.936  80.3  2.7792   24  666.0   \n",
       "\n",
       "     PTRATIO       B  LSTAT  MEDV  \n",
       "198     12.6  392.20   6.62  34.6  \n",
       "317     18.4  396.90  15.94  19.8  \n",
       "502     21.0  396.90   9.08  20.6  \n",
       "456     20.2   10.48  19.01  12.7  \n",
       "457     20.2    3.50  16.94  13.5  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sample(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1f5d165a",
   "metadata": {},
   "source": [
    "## Understanding data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "28dff945",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(506, 14)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "344feace",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<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>B</th>\n",
       "      <th>LSTAT</th>\n",
       "      <th>MEDV</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.613524</td>\n",
       "      <td>11.363636</td>\n",
       "      <td>11.136779</td>\n",
       "      <td>0.069170</td>\n",
       "      <td>0.554695</td>\n",
       "      <td>6.284634</td>\n",
       "      <td>68.574901</td>\n",
       "      <td>3.795043</td>\n",
       "      <td>9.549407</td>\n",
       "      <td>408.237154</td>\n",
       "      <td>18.455534</td>\n",
       "      <td>356.674032</td>\n",
       "      <td>12.653063</td>\n",
       "      <td>22.532806</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>8.601545</td>\n",
       "      <td>23.322453</td>\n",
       "      <td>6.860353</td>\n",
       "      <td>0.253994</td>\n",
       "      <td>0.115878</td>\n",
       "      <td>0.702617</td>\n",
       "      <td>28.148861</td>\n",
       "      <td>2.105710</td>\n",
       "      <td>8.707259</td>\n",
       "      <td>168.537116</td>\n",
       "      <td>2.164946</td>\n",
       "      <td>91.294864</td>\n",
       "      <td>7.141062</td>\n",
       "      <td>9.197104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.006320</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.460000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.385000</td>\n",
       "      <td>3.561000</td>\n",
       "      <td>2.900000</td>\n",
       "      <td>1.129600</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>187.000000</td>\n",
       "      <td>12.600000</td>\n",
       "      <td>0.320000</td>\n",
       "      <td>1.730000</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.082045</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.190000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.449000</td>\n",
       "      <td>5.885500</td>\n",
       "      <td>45.025000</td>\n",
       "      <td>2.100175</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>279.000000</td>\n",
       "      <td>17.400000</td>\n",
       "      <td>375.377500</td>\n",
       "      <td>6.950000</td>\n",
       "      <td>17.025000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.256510</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>9.690000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.538000</td>\n",
       "      <td>6.208500</td>\n",
       "      <td>77.500000</td>\n",
       "      <td>3.207450</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>330.000000</td>\n",
       "      <td>19.050000</td>\n",
       "      <td>391.440000</td>\n",
       "      <td>11.360000</td>\n",
       "      <td>21.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>3.677083</td>\n",
       "      <td>12.500000</td>\n",
       "      <td>18.100000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.624000</td>\n",
       "      <td>6.623500</td>\n",
       "      <td>94.075000</td>\n",
       "      <td>5.188425</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>666.000000</td>\n",
       "      <td>20.200000</td>\n",
       "      <td>396.225000</td>\n",
       "      <td>16.955000</td>\n",
       "      <td>25.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>88.976200</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>27.740000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.871000</td>\n",
       "      <td>8.780000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>12.126500</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>711.000000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>396.900000</td>\n",
       "      <td>37.970000</td>\n",
       "      <td>50.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "             CRIM          ZN       INDUS        CHAS         NOX          RM  \\\n",
       "count  506.000000  506.000000  506.000000  506.000000  506.000000  506.000000   \n",
       "mean     3.613524   11.363636   11.136779    0.069170    0.554695    6.284634   \n",
       "std      8.601545   23.322453    6.860353    0.253994    0.115878    0.702617   \n",
       "min      0.006320    0.000000    0.460000    0.000000    0.385000    3.561000   \n",
       "25%      0.082045    0.000000    5.190000    0.000000    0.449000    5.885500   \n",
       "50%      0.256510    0.000000    9.690000    0.000000    0.538000    6.208500   \n",
       "75%      3.677083   12.500000   18.100000    0.000000    0.624000    6.623500   \n",
       "max     88.976200  100.000000   27.740000    1.000000    0.871000    8.780000   \n",
       "\n",
       "              AGE         DIS         RAD         TAX     PTRATIO           B  \\\n",
       "count  506.000000  506.000000  506.000000  506.000000  506.000000  506.000000   \n",
       "mean    68.574901    3.795043    9.549407  408.237154   18.455534  356.674032   \n",
       "std     28.148861    2.105710    8.707259  168.537116    2.164946   91.294864   \n",
       "min      2.900000    1.129600    1.000000  187.000000   12.600000    0.320000   \n",
       "25%     45.025000    2.100175    4.000000  279.000000   17.400000  375.377500   \n",
       "50%     77.500000    3.207450    5.000000  330.000000   19.050000  391.440000   \n",
       "75%     94.075000    5.188425   24.000000  666.000000   20.200000  396.225000   \n",
       "max    100.000000   12.126500   24.000000  711.000000   22.000000  396.900000   \n",
       "\n",
       "            LSTAT        MEDV  \n",
       "count  506.000000  506.000000  \n",
       "mean    12.653063   22.532806  \n",
       "std      7.141062    9.197104  \n",
       "min      1.730000    5.000000  \n",
       "25%      6.950000   17.025000  \n",
       "50%     11.360000   21.200000  \n",
       "75%     16.955000   25.000000  \n",
       "max     37.970000   50.000000  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "d9e9c035",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CRIM       0\n",
       "ZN         0\n",
       "INDUS      0\n",
       "CHAS       0\n",
       "NOX        0\n",
       "RM         0\n",
       "AGE        0\n",
       "DIS        0\n",
       "RAD        0\n",
       "TAX        0\n",
       "PTRATIO    0\n",
       "B          0\n",
       "LSTAT      0\n",
       "MEDV       0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "b759bb5e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Correlation matrix\n",
    "corr_matrix = df.corr()\n",
    "plt.figure(figsize=(10, 8))\n",
    "sns.heatmap(corr_matrix, annot=True, cmap='coolwarm')\n",
    "plt.title('Correlation Matrix of Boston Housing Features')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "08d0202f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Distribution of target variable (MEDV)\n",
    "plt.figure(figsize=(6, 4))\n",
    "sns.histplot(df['MEDV'], bins=30, kde=True)\n",
    "plt.title('Distribution of MEDV (Median value of owner-occupied homes)')\n",
    "plt.xlabel('MEDV ($1000s)')\n",
    "plt.ylabel('Frequency')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "b482512d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Relationship between RM (average number of rooms) and MEDV\n",
    "plt.figure(figsize=(6, 4))\n",
    "sns.scatterplot(x='RM', y='MEDV', data=df)\n",
    "plt.title('Relationship between RM and MEDV')\n",
    "plt.xlabel('Average Number of Rooms per Dwelling (RM)')\n",
    "plt.ylabel('Median value of owner-occupied homes (MEDV)')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "313e931c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Examining the effect of CHAS (Charles River dummy variable) on MEDV\n",
    "plt.figure(figsize=(6, 4))\n",
    "sns.boxplot(x='CHAS', y='MEDV', data=df)\n",
    "plt.title('MEDV by CHAS (Charles River dummy variable)')\n",
    "plt.xlabel('Charles River dummy variable (CHAS)')\n",
    "plt.ylabel('Median value of owner-occupied homes (MEDV)')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "4d2fb33e",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score\n",
    "\n",
    "# Assuming 'MEDV' is the target variable and the rest are predictors\n",
    "X = df.drop('MEDV', axis=1)\n",
    "y = df['MEDV']\n",
    "\n",
    "# Splitting the dataset into training and testing sets\n",
    "X_train, X_test, y_train, y_test = train_test_split(X,\n",
    "                                                    y,\n",
    "                                                    test_size=0.2,\n",
    "                                                    random_state=42)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "39a59b4f",
   "metadata": {},
   "source": [
    "## Data normalization"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7844c2f6",
   "metadata": {},
   "source": [
    "For the Boston Housing dataset, normalization can help improve model performance, especially for algorithms that are sensitive to the scale of the data, such as Support Vector Machines, K-nearest Neighbors, and models that use gradient descent for optimization. Two common normalization techniques are:\n",
    "\n",
    "- **Min-Max Scaling**: This technique rescales the data to a fixed range, usually 0 to 1. It's useful when you need to normalize the range of the features.\n",
    "\n",
    "- **Standardization (Z-score Normalization)**: This technique rescales the data so that it has a mean of 0 and a standard deviation of 1. It's useful in many scenarios, especially when the algorithm assumes that the input features are normally distributed.\n",
    "\n",
    "For the Boston Housing dataset, standardization is often preferred since it deals well with features that have different units of measurement and scales. Here’s how you can apply standardization to the dataset:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "5c39e869",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "# Initialize the StandardScaler\n",
    "scaler = StandardScaler()\n",
    "\n",
    "# Fit on the training data and transform both training and testing data\n",
    "X_train_scaled = scaler.fit_transform(X_train)\n",
    "X_test_scaled = scaler.transform(X_test)\n",
    "\n",
    "# Convert scaled arrays back to DataFrame for convenience\n",
    "X_train_scaled = pd.DataFrame(X_train_scaled, columns=X_train.columns)\n",
    "X_test_scaled = pd.DataFrame(X_test_scaled, columns=X_test.columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "74b82799",
   "metadata": {},
   "source": [
    "__Attention: The above normalization is not used in the rest of the notebook!__"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aec45280",
   "metadata": {},
   "source": [
    "## Linear Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "ad9eb918",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Absolute Error (MAE): 3.1890919658879326\n",
      "Mean Squared Error (MSE): 24.29111947497418\n",
      "Root Mean Squared Error (RMSE): 4.928602182665403\n",
      "R^2 Score: 0.6687594935356229\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "# Initializing and fitting the Linear Regression model\n",
    "model = LinearRegression()\n",
    "model.fit(X_train, y_train)\n",
    "\n",
    "# Making predictions on the test set\n",
    "y_pred = model.predict(X_test)\n",
    "\n",
    "# Calculating evaluation metrics\n",
    "mae = mean_absolute_error(y_test, y_pred)\n",
    "mse = mean_squared_error(y_test, y_pred)\n",
    "rmse = np.sqrt(mse)  # Root Mean Squared Error\n",
    "r2 = r2_score(y_test, y_pred)\n",
    "\n",
    "print(f\"Mean Absolute Error (MAE): {mae}\")\n",
    "print(f\"Mean Squared Error (MSE): {mse}\")\n",
    "print(f\"Root Mean Squared Error (RMSE): {rmse}\")\n",
    "print(f\"R^2 Score: {r2}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c7541223",
   "metadata": {},
   "source": [
    "## Ridge Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "048043e2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Absolute Error (MAE): 3.1329474278055347\n",
      "Mean Squared Error (MSE): 24.477191227708662\n",
      "Root Mean Squared Error (RMSE): 4.947442897872461\n",
      "R^2 Score: 0.6662221670168521\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import Ridge\n",
    "\n",
    "# Initialize and train Ridge Regression model\n",
    "ridge_model = Ridge(alpha=1.0)\n",
    "ridge_model.fit(X_train, y_train)\n",
    "\n",
    "# Predict on test set\n",
    "y_pred = ridge_model.predict(X_test)\n",
    "\n",
    "# Evaluation metrics\n",
    "mae = mean_absolute_error(y_test, y_pred)\n",
    "mse = mean_squared_error(y_test, y_pred)\n",
    "rmse = np.sqrt(mse)\n",
    "r2 = r2_score(y_test, y_pred)\n",
    "\n",
    "print(f\"Mean Absolute Error (MAE): {mae}\")\n",
    "print(f\"Mean Squared Error (MSE): {mse}\")\n",
    "print(f\"Root Mean Squared Error (RMSE): {rmse}\")\n",
    "print(f\"R^2 Score: {r2}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8e27d2db",
   "metadata": {},
   "source": [
    "## Lasso Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "54977392",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Absolute Error (MAE): 3.2535397673681636\n",
      "Mean Squared Error (MSE): 24.409489761299707\n",
      "Root Mean Squared Error (RMSE): 4.940596093721861\n",
      "R^2 Score: 0.6671453631686304\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import Lasso\n",
    "\n",
    "# Initialize and train Lasso Regression model\n",
    "lasso_model = Lasso(alpha=1.0)\n",
    "lasso_model.fit(X_train, y_train)\n",
    "\n",
    "# Predict on test set\n",
    "y_pred = lasso_model.predict(X_test)\n",
    "\n",
    "# Evaluation metrics\n",
    "mae = mean_absolute_error(y_test, y_pred)\n",
    "mse = mean_squared_error(y_test, y_pred)\n",
    "rmse = np.sqrt(mse)\n",
    "r2 = r2_score(y_test, y_pred)\n",
    "\n",
    "print(f\"Mean Absolute Error (MAE): {mae}\")\n",
    "print(f\"Mean Squared Error (MSE): {mse}\")\n",
    "print(f\"Root Mean Squared Error (RMSE): {rmse}\")\n",
    "print(f\"R^2 Score: {r2}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4182af9c",
   "metadata": {},
   "source": [
    "## ElasticNet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "364fc298",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Absolute Error (MAE): 3.244000641536975\n",
      "Mean Squared Error (MSE): 23.968733210204793\n",
      "Root Mean Squared Error (RMSE): 4.89578729217322\n",
      "R^2 Score: 0.6731556429073877\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import ElasticNet\n",
    "\n",
    "# Initialize and train ElasticNet model\n",
    "elastic_net_model = ElasticNet(random_state=42)\n",
    "elastic_net_model.fit(X_train, y_train)\n",
    "\n",
    "# Predict on test set\n",
    "y_pred = elastic_net_model.predict(X_test)\n",
    "\n",
    "# Evaluation metrics\n",
    "mae = mean_absolute_error(y_test, y_pred)\n",
    "mse = mean_squared_error(y_test, y_pred)\n",
    "rmse = np.sqrt(mse)\n",
    "r2 = r2_score(y_test, y_pred)\n",
    "\n",
    "print(f\"Mean Absolute Error (MAE): {mae}\")\n",
    "print(f\"Mean Squared Error (MSE): {mse}\")\n",
    "print(f\"Root Mean Squared Error (RMSE): {rmse}\")\n",
    "print(f\"R^2 Score: {r2}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ada39d0d",
   "metadata": {},
   "source": [
    "## Support Vector Regression (SVR)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "6c493ddc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Absolute Error (MAE): 2.7398756050247868\n",
      "Mean Squared Error (MSE): 25.600625317621695\n",
      "Root Mean Squared Error (RMSE): 5.059706050515356\n",
      "R^2 Score: 0.6509027052149569\n"
     ]
    }
   ],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.svm import SVR\n",
    "from sklearn.pipeline import make_pipeline\n",
    "\n",
    "# Initialize and train Support Vector Regression model with feature scaling\n",
    "svr_model = make_pipeline(StandardScaler(), SVR(C=1.0, epsilon=0.2))\n",
    "svr_model.fit(X_train, y_train)\n",
    "\n",
    "# Predict on test set\n",
    "y_pred = svr_model.predict(X_test)\n",
    "\n",
    "# Evaluation metrics\n",
    "mae = mean_absolute_error(y_test, y_pred)\n",
    "mse = mean_squared_error(y_test, y_pred)\n",
    "rmse = np.sqrt(mse)\n",
    "r2 = r2_score(y_test, y_pred)\n",
    "\n",
    "print(f\"Mean Absolute Error (MAE): {mae}\")\n",
    "print(f\"Mean Squared Error (MSE): {mse}\")\n",
    "print(f\"Root Mean Squared Error (RMSE): {rmse}\")\n",
    "print(f\"R^2 Score: {r2}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b1c2e5c6",
   "metadata": {},
   "source": [
    "## Why Feature Scaling for SVR?\n",
    "\n",
    "Feature scaling was performed before training the Support Vector Regression (SVR) model due to the nature of the algorithm itself. SVR, like many machine learning algorithms, is sensitive to the range of input features. Here are the reasons why feature scaling is essential for SVR:\n",
    "\n",
    "- **Sensitivity to Feature Scales**: SVR uses the Euclidean distance between data points in its objective function. If one feature has a very large range compared to others, it will dominate the distance calculation, leading to skewed results. Scaling ensures that all features contribute equally to the distance calculation.\n",
    "\n",
    "- **Optimization Efficiency**: The SVR model's optimization algorithms converge faster when features are on similar scales. Without scaling, features with larger ranges might slow down the training process and lead to longer convergence times or convergence to suboptimal solutions.\n",
    "\n",
    "- **Regularization Impact**: SVR often includes regularization terms to prevent overfitting. The regularization term's effectiveness can be disproportionately affected by features of different scales, making it crucial to scale the features for balanced regularization across all dimensions."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "17530014",
   "metadata": {},
   "source": [
    "## Decision Tree Regressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "495b30f1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Absolute Error (MAE): 2.394117647058824\n",
      "Mean Squared Error (MSE): 10.416078431372549\n",
      "Root Mean Squared Error (RMSE): 3.2273949915330395\n",
      "R^2 Score: 0.8579634380978161\n"
     ]
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeRegressor\n",
    "\n",
    "# Initialize and train Decision Tree Regressor\n",
    "tree_model = DecisionTreeRegressor(random_state=42)\n",
    "tree_model.fit(X_train, y_train)\n",
    "\n",
    "# Predict on test set\n",
    "y_pred = tree_model.predict(X_test)\n",
    "\n",
    "# Evaluation metrics\n",
    "mae = mean_absolute_error(y_test, y_pred)\n",
    "mse = mean_squared_error(y_test, y_pred)\n",
    "rmse = np.sqrt(mse)\n",
    "r2 = r2_score(y_test, y_pred)\n",
    "\n",
    "print(f\"Mean Absolute Error (MAE): {mae}\")\n",
    "print(f\"Mean Squared Error (MSE): {mse}\")\n",
    "print(f\"Root Mean Squared Error (RMSE): {rmse}\")\n",
    "print(f\"R^2 Score: {r2}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c45f56d3",
   "metadata": {},
   "source": [
    "## Random Forest Regressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "1021d70c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Absolute Error (MAE): 2.0395392156862746\n",
      "Mean Squared Error (MSE): 7.901513892156864\n",
      "Root Mean Squared Error (RMSE): 2.8109631609391226\n",
      "R^2 Score: 0.8922527442109116\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "\n",
    "# Initialize and train Random Forest Regressor\n",
    "random_forest_model = RandomForestRegressor(random_state=42)\n",
    "random_forest_model.fit(X_train, y_train)\n",
    "\n",
    "# Predict on test set\n",
    "y_pred = random_forest_model.predict(X_test)\n",
    "\n",
    "# Evaluation metrics\n",
    "mae = mean_absolute_error(y_test, y_pred)\n",
    "mse = mean_squared_error(y_test, y_pred)\n",
    "rmse = np.sqrt(mse)\n",
    "r2 = r2_score(y_test, y_pred)\n",
    "\n",
    "print(f\"Mean Absolute Error (MAE): {mae}\")\n",
    "print(f\"Mean Squared Error (MSE): {mse}\")\n",
    "print(f\"Root Mean Squared Error (RMSE): {rmse}\")\n",
    "print(f\"R^2 Score: {r2}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ee278754",
   "metadata": {},
   "source": [
    "## Gradient Boosting Regressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "6224d035",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Absolute Error (MAE): 1.9122427713612915\n",
      "Mean Squared Error (MSE): 6.208861361528038\n",
      "Root Mean Squared Error (RMSE): 2.491758688462436\n",
      "R^2 Score: 0.9153342280466539\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import GradientBoostingRegressor\n",
    "\n",
    "# Initialize and train Gradient Boosting Regressor\n",
    "gradient_boosting_model = GradientBoostingRegressor(random_state=42)\n",
    "gradient_boosting_model.fit(X_train, y_train)\n",
    "\n",
    "# Predict on test set\n",
    "y_pred = gradient_boosting_model.predict(X_test)\n",
    "\n",
    "# Evaluation metrics\n",
    "mae = mean_absolute_error(y_test, y_pred)\n",
    "mse = mean_squared_error(y_test, y_pred)\n",
    "rmse = np.sqrt(mse)\n",
    "r2 = r2_score(y_test, y_pred)\n",
    "\n",
    "print(f\"Mean Absolute Error (MAE): {mae}\")\n",
    "print(f\"Mean Squared Error (MSE): {mse}\")\n",
    "print(f\"Root Mean Squared Error (RMSE): {rmse}\")\n",
    "print(f\"R^2 Score: {r2}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8828be6f",
   "metadata": {},
   "source": [
    "## Putting everything together!"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "32575002",
   "metadata": {},
   "source": [
    "The following 3 cells are used to compare the regression models we build before. It achieves this through several steps:\n",
    "\n",
    "1. **Define Models:** We initialize a dictionary named `models` containing multiple regression models, including Linear Regression, Ridge, Lasso, ElasticNet, Support Vector Regression (SVR), Decision Tree, Random Forest Regressor, and Gradient Boosting Regressor. The SVR model is specifically scaled using `StandardScaler` to standardize features by removing the mean and scaling to unit variance.\n",
    "\n",
    "2. **Evaluate Models:** The code loops through each model, fitting them on training data (`X_train`, `y_train`) and predicting outcomes on test data (`X_test`). It then calculates Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and the R^2 score for each model, storing these metrics in a `metrics_results` dictionary.\n",
    "\n",
    "3. **Visualization:** Two plots are generated to visually compare the models' performances. The first plot shows MAE, MSE, and RMSE for each model, providing insights into their prediction errors. The second plot focuses on the R^2 score, which indicates how well the models capture the variance of the target variable. Bar plots are used for clear, comparative visualization."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "be31cb5b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define models\n",
    "models = {\n",
    "    'Linear Regression': LinearRegression(),\n",
    "    'Ridge': Ridge(),\n",
    "    'Lasso': Lasso(),\n",
    "    'ElasticNet': ElasticNet(),\n",
    "    'SVR': make_pipeline(StandardScaler(), SVR(C=1.0, epsilon=0.2)),\n",
    "    'Decision Tree': DecisionTreeRegressor(),\n",
    "    'Random Forest Regresor': RandomForestRegressor(),\n",
    "    'Gradient Boosting': GradientBoostingRegressor()\n",
    "}\n",
    "\n",
    "# Placeholder for metric results\n",
    "metrics_results = {'Model': [], 'MAE': [], 'MSE': [], 'RMSE': [], 'R2': []}\n",
    "\n",
    "# Evaluate each model\n",
    "for name, model in models.items():\n",
    "    model.fit(X_train, y_train)\n",
    "    y_pred = model.predict(X_test)\n",
    "    metrics_results['Model'].append(name)\n",
    "    metrics_results['MAE'].append(mean_absolute_error(y_test, y_pred))\n",
    "    metrics_results['MSE'].append(mean_squared_error(y_test, y_pred))\n",
    "    metrics_results['RMSE'].append(np.sqrt(mean_squared_error(y_test, y_pred)))\n",
    "    metrics_results['R2'].append(r2_score(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "a56edd1a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1400x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(14, 8))\n",
    "bar_width = 0.25\n",
    "index = np.arange(len(models))\n",
    "\n",
    "for i, metric in enumerate(['MAE', 'MSE', 'RMSE']):\n",
    "    ax.bar(index + i * bar_width,\n",
    "           metrics_results[metric],\n",
    "           bar_width,\n",
    "           label=metric)\n",
    "\n",
    "ax.set_xlabel('Model')\n",
    "ax.set_ylabel('Score')\n",
    "ax.set_title('MAE, MSE, and RMSE by Model')\n",
    "ax.set_xticks(index +\n",
    "              bar_width)  # Adjusted to center ticks among the group of bars\n",
    "ax.set_xticklabels(metrics_results['Model'], rotation=45, ha=\"right\")\n",
    "ax.legend()\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.grid(axis='y')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "e7ad5153",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(10, 6))\n",
    "\n",
    "bar_positions = np.arange(len(metrics_results['Model']))\n",
    "ax.bar(bar_positions,\n",
    "       metrics_results['R2'],\n",
    "       color='skyblue',\n",
    "       label='R^2 Score')\n",
    "\n",
    "ax.set_xlabel('Model')\n",
    "ax.set_ylabel('R^2 Score')\n",
    "ax.set_title('R^2 Scores by Model')\n",
    "ax.set_xticks(bar_positions)  # Explicitly setting tick positions\n",
    "ax.set_xticklabels(metrics_results['Model'], rotation=45, ha=\"right\")\n",
    "ax.legend()\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "f958c697",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 192 candidates, totalling 960 fits\n",
      "Best Parameters: {'learning_rate': 0.2, 'max_depth': 3, 'min_samples_split': 4, 'n_estimators': 100}\n",
      "Best Score (MSE): -12.579331231930643\n",
      "Mean Absolute Error (MAE): 1.768828945927315\n",
      "Mean Squared Error (MSE): 5.427450486871088\n",
      "Root Mean Squared Error (RMSE): 2.329688924914888\n",
      "R^2 Score: 0.9259897655217713\n",
      "Total time elapsed for Grid Search: 22.067198991775513 seconds\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "\"\\n# Plotting the outcome of various parameter sets\\nresults = grid_search.cv_results_\\nmean_test_scores = -results['mean_test_score']\\nparams = [str(param) for param in results['params']]\\n\\nplt.figure(figsize=(10, 6))\\nplt.barh(params, mean_test_scores, color='skyblue')\\nplt.xlabel('Negative Mean Squared Error')\\nplt.ylabel('Parameter Set')\\nplt.title('Performance of Parameter Sets in Grid Search')\\nplt.show()\""
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "import time\n",
    "import joblib\n",
    "\n",
    "# Define parameter grid\n",
    "param_grid = {\n",
    "    'n_estimators': [50, 100, 200, 300],\n",
    "    'learning_rate': [0.01, 0.05, 0.1, 0.2],\n",
    "    'max_depth': [3, 4, 5, 6],\n",
    "    'min_samples_split': [2, 4, 6]\n",
    "}\n",
    "\n",
    "# Initialize the GridSearchCV object\n",
    "grid_search = GridSearchCV(\n",
    "    estimator=GradientBoostingRegressor(random_state=42),\n",
    "    param_grid=param_grid,\n",
    "    scoring='neg_mean_squared_error',\n",
    "    cv=5,\n",
    "    verbose=2,\n",
    "    n_jobs=-1)\n",
    "\n",
    "# Measure the time taken for grid search\n",
    "start_time = time.time()\n",
    "\n",
    "# Perform grid search\n",
    "grid_search.fit(X_train, y_train)\n",
    "\n",
    "# Calculate elapsed time\n",
    "elapsed_time = time.time() - start_time\n",
    "\n",
    "# Best parameters and best score\n",
    "best_params = grid_search.best_params_\n",
    "best_score = grid_search.best_score_\n",
    "\n",
    "# After finding the best model, save it to a file\n",
    "best_model_filename = 'best_model_GS.joblib'\n",
    "joblib.dump(grid_search.best_estimator_, best_model_filename)\n",
    "#loaded_model = joblib.load('best_model_GS.joblib')\n",
    "\n",
    "# Predict on test set using the best found model\n",
    "y_pred = grid_search.predict(X_test)\n",
    "\n",
    "# Evaluation metrics\n",
    "mae = mean_absolute_error(y_test, y_pred)\n",
    "mse = mean_squared_error(y_test, y_pred)\n",
    "rmse = np.sqrt(mse)\n",
    "r2 = r2_score(y_test, y_pred)\n",
    "\n",
    "print(f\"Best Parameters: {best_params}\")\n",
    "print(f\"Best Score (MSE): {best_score}\")\n",
    "print(f\"Mean Absolute Error (MAE): {mae}\")\n",
    "print(f\"Mean Squared Error (MSE): {mse}\")\n",
    "print(f\"Root Mean Squared Error (RMSE): {rmse}\")\n",
    "print(f\"R^2 Score: {r2}\")\n",
    "print(f\"Total time elapsed for Grid Search: {elapsed_time} seconds\")\n",
    "\"\"\"\n",
    "# Plotting the outcome of various parameter sets\n",
    "results = grid_search.cv_results_\n",
    "mean_test_scores = -results['mean_test_score']\n",
    "params = [str(param) for param in results['params']]\n",
    "\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.barh(params, mean_test_scores, color='skyblue')\n",
    "plt.xlabel('Negative Mean Squared Error')\n",
    "plt.ylabel('Parameter Set')\n",
    "plt.title('Performance of Parameter Sets in Grid Search')\n",
    "plt.show()\"\"\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "79f92f7b",
   "metadata": {},
   "source": [
    "## Rationale Behind Using Negative Mean Squared Error (Negative MSE) in Grid Search\n",
    "\n",
    "When performing hyperparameter optimization with grid search in scikit-learn, we often use scoring metrics to evaluate the performance of different parameter combinations. The choice of \"Negative Mean Squared Error (Negative MSE)\" as a scoring metric might seem counterintuitive at first, but there's a practical reason for its use:\n",
    "\n",
    "### Optimization Objective\n",
    "\n",
    "Scikit-learn's grid search (`GridSearchCV`) aims to *maximize* the specified scoring metric. Since many regression metrics (like MSE) are naturally framed as something to minimize (you want the error to be as small as possible), directly using MSE would conflict with the grid search's objective of maximization. To align with this objective, we use negative MSE instead.\n",
    "\n",
    "### Negative MSE Explained\n",
    "\n",
    "- **Mean Squared Error (MSE)** measures the average squared difference between the predicted and actual values. A lower MSE indicates a better model fit, as it signifies smaller errors between predicted and actual values.\n",
    "- **Negative MSE** simply takes the negative of the MSE values. This transformation turns a minimization problem into a maximization one, fitting into the grid search framework where higher scores are better. By using negative MSE, the grid search can operate under its default maximization behavior, selecting the model parameters that yield the highest (least negative) score.\n",
    "\n",
    "### Implications for Model Selection\n",
    "\n",
    "By using Negative MSE as the scoring metric, we can efficiently utilize grid search to find the optimal model parameters that minimize the MSE, since grid search will inherently select the parameter set with the highest score (or the least negative MSE, which corresponds to the lowest actual MSE).\n",
    "\n",
    "In summary, the use of Negative MSE as a scoring metric is a practical workaround that adapts the minimization objective of reducing error into the maximization framework used by scikit-learn's grid search, allowing for straightforward and effective model optimization.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "26d28164",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 10 candidates, totalling 50 fits\n",
      "Best Parameters: {'n_estimators': 100, 'min_samples_split': 4, 'max_depth': 4, 'learning_rate': 0.1}\n",
      "Best Score (MSE): -13.79660240986566\n",
      "Mean Absolute Error (MAE): 1.9149312428221237\n",
      "Mean Squared Error (MSE): 7.016245414956372\n",
      "Root Mean Squared Error (RMSE): 2.6488196267311923\n",
      "R^2 Score: 0.9043245130335442\n",
      "Total time elapsed for Random Search: 1.0492496490478516 seconds\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "\n",
    "# Define parameter distribution\n",
    "param_dist = {\n",
    "    'n_estimators': [50, 100, 200, 300],\n",
    "    'learning_rate': [0.01, 0.05, 0.1, 0.2],\n",
    "    'max_depth': [3, 4, 5, 6],\n",
    "    'min_samples_split': [2, 4, 6]\n",
    "}\n",
    "\n",
    "# Initialize the RandomizedSearchCV object\n",
    "random_search = RandomizedSearchCV(\n",
    "    estimator=GradientBoostingRegressor(random_state=42),\n",
    "    param_distributions=param_dist,\n",
    "    n_iter=10,  # Number of parameter settings to sample\n",
    "    scoring='neg_mean_squared_error',\n",
    "    cv=5,\n",
    "    verbose=2,\n",
    "    random_state=42,\n",
    "    n_jobs=-1)\n",
    "\n",
    "# Measure the time taken for random search\n",
    "start_time = time.time()\n",
    "\n",
    "# Perform random search\n",
    "random_search.fit(X_train, y_train)\n",
    "\n",
    "# Calculate elapsed time\n",
    "elapsed_time = time.time() - start_time\n",
    "\n",
    "# Best parameters and best score\n",
    "best_params = random_search.best_params_\n",
    "best_score = random_search.best_score_\n",
    "\n",
    "# After finding the best model, save it to a file\n",
    "best_model_filename = 'best_model_RS.joblib'\n",
    "joblib.dump(grid_search.best_estimator_, best_model_filename)\n",
    "#loaded_model = joblib.load('best_model_RS.joblib')\n",
    "\n",
    "# Predict on test set using the best found model\n",
    "y_pred = random_search.predict(X_test)\n",
    "\n",
    "# Evaluation metrics\n",
    "mae = mean_absolute_error(y_test, y_pred)\n",
    "mse = mean_squared_error(y_test, y_pred)\n",
    "rmse = np.sqrt(mse)\n",
    "r2 = r2_score(y_test, y_pred)\n",
    "\n",
    "print(f\"Best Parameters: {best_params}\")\n",
    "print(f\"Best Score (MSE): {best_score}\")\n",
    "print(f\"Mean Absolute Error (MAE): {mae}\")\n",
    "print(f\"Mean Squared Error (MSE): {mse}\")\n",
    "print(f\"Root Mean Squared Error (RMSE): {rmse}\")\n",
    "print(f\"R^2 Score: {r2}\")\n",
    "print(f\"Total time elapsed for Random Search: {elapsed_time} seconds\")\n",
    "\n",
    "# Plotting the outcome of various parameter sets\n",
    "results = random_search.cv_results_\n",
    "mean_test_scores = -results['mean_test_score']\n",
    "params = [str(param) for param in results['params']]\n",
    "\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.barh(params, mean_test_scores, color='skyblue')\n",
    "plt.xlabel('Negative Mean Squared Error')\n",
    "plt.ylabel('Parameter Set')\n",
    "plt.title('Performance of Parameter Sets in Random Search')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "90c78820",
   "metadata": {},
   "source": [
    "## Understanding Random Search and Its Difference from Grid Search\n",
    "\n",
    "Two popular methods for hyperparameter optimization are Grid Search and Random Search. Here's an overview of Random Search, how it operates, and how it contrasts with Grid Search.\n",
    "\n",
    "### What is Random Search?\n",
    "\n",
    "Random Search is a hyperparameter optimization technique that selects random combinations of hyperparameter values from a specified range for each hyperparameter. This approach evaluates a fixed number of random combinations by training the model with them and selecting the combination that performs the best based on a chosen performance metric.\n",
    "\n",
    "### How Random Search Works\n",
    "\n",
    "1. **Define the Hyperparameter Space:** Unlike Grid Search, which requires explicit lists of values for each hyperparameter, Random Search works with distributions (for continuous hyperparameters) or lists (for categorical hyperparameters) to sample from.\n",
    "2. **Random Sampling:** It randomly samples combinations of hyperparameters from the defined space.\n",
    "3. **Model Evaluation:** Each randomly selected combination is used to train a model, and its performance is assessed via cross-validation.\n",
    "4. **Selection of Best Parameters:** After evaluating a predetermined number of random combinations, the set of hyperparameters yielding the best evaluation metric is chosen.\n",
    "\n",
    "### Random Search vs. Grid Search\n",
    "\n",
    "- **Exploration of Hyperparameter Space:** Grid Search systematically explores all possible combinations in the defined hyperparameter grid, which can be computationally expensive and time-consuming, especially with a large number of hyperparameters or large ranges of values. Random Search, on the other hand, samples a subset of combinations, potentially exploring the space more efficiently.\n",
    "- **Computational Efficiency:** Random Search can be more computationally efficient than Grid Search, particularly when the optimal hyperparameter values lie within a narrow region, because it doesn't waste time evaluating unpromising regions of the space.\n",
    "- **Performance:** While Grid Search is exhaustive and guaranteed to find the optimal combination in the grid, Random Search has a chance (though not guaranteed) to find a near-optimal combination with far fewer iterations. Studies have shown that Random Search can be surprisingly effective compared to Grid Search, especially in high-dimensional spaces.\n",
    "- **Flexibility:** Random Search allows for the specification of distributions for continuous hyperparameters, providing a more flexible approach to exploring the hyperparameter space."
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