2. Linear Regression/2. Feature Selection.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# **Linear Regression**: Human Brain Weight 🧠"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This dataset was compiled using a medical study conducted on a group of people. This dataset shows a few variations of head sizes and masses of brains, it also consists additional gender and age group columns. The goal of the study is to predict the masses of the brains using the data, and later compare it with the actual masses of brains mentioned in the dataset."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# **Feature Selection**\n",
    "\n",
    "$\\qquad$ <span style=\"color:gray\"><b>0.</b> Settings </span><br>\n",
    "$\\qquad$ <span style=\"color:gray\"><b>1.</b> Dataset </span><br>\n",
    "$\\qquad$ <span style=\"color:gray\"><b>2.</b> Data Preprocessing </span><br>\n",
    "$\\qquad$ <span style=\"color:gray\"><b>3.</b> Feature Selection </span><br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## **0.** Settings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Libraries\n",
    "import matplotlib.pyplot as plt \n",
    "import seaborn as sns \n",
    "import pandas as pd  \n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## **1.** Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "'''\n",
    "    DATASET INFORMATIONS\n",
    "\n",
    "    |--------------|------------|-------|--------------------------------------------------------------|\n",
    "    | Name         | Data Type  | Meas. | Description                                                  |\n",
    "    |--------------|------------|-------|--------------------------------------------------------------|\n",
    "    | Gender       | nominal    |       | 1 represents Male, 2 represents Female                       |\n",
    "    | Age Range    | nominal    |       | 1 represents >18 years of age, 2 represents <18 years of age |\n",
    "    | Head Size    | continuous | cm^3  | Head volume                                                  |\n",
    "    | Brain Weight | continuous | grams | Mass of brains                                               |\n",
    "    |--------------|------------|-------|--------------------------------------------------------------|  \n",
    "\n",
    "'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>Gender</th>\n",
       "      <th>Age Range</th>\n",
       "      <th>Head Size(cm^3)</th>\n",
       "      <th>Brain Weight(grams)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4512</td>\n",
       "      <td>1530</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3738</td>\n",
       "      <td>1297</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4261</td>\n",
       "      <td>1335</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3777</td>\n",
       "      <td>1282</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4177</td>\n",
       "      <td>1590</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Gender  Age Range  Head Size(cm^3)  Brain Weight(grams)\n",
       "0       1          1             4512                 1530\n",
       "1       1          1             3738                 1297\n",
       "2       1          1             4261                 1335\n",
       "3       1          1             3777                 1282\n",
       "4       1          1             4177                 1590"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Import the dataset\n",
    "data = pd.read_csv('./dataset.csv')\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Gender', 'Age Range', 'Head Size(cm^3)', 'Brain Weight(grams)'], dtype='object')"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Gender', 'Age Range', 'Head Size', 'Brain Weight'], dtype='object')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.columns = ['Gender', 'Age Range', 'Head Size', 'Brain Weight']\n",
    "data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 237 entries, 0 to 236\n",
      "Data columns (total 4 columns):\n",
      " #   Column        Non-Null Count  Dtype\n",
      "---  ------        --------------  -----\n",
      " 0   Gender        237 non-null    int64\n",
      " 1   Age Range     237 non-null    int64\n",
      " 2   Head Size     237 non-null    int64\n",
      " 3   Brain Weight  237 non-null    int64\n",
      "dtypes: int64(4)\n",
      "memory usage: 7.5 KB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## **2.** Data Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Gender          0\n",
       "Age Range       0\n",
       "Head Size       0\n",
       "Brain Weight    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Null elements\n",
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are no missing values in this dataset."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## **3.** Feature Selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 576x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Correlation matrix\n",
    "correlation_matrix = data.corr().round(2)\n",
    "\n",
    "plt.figure(figsize=(8,5));\n",
    "sns.heatmap(data=correlation_matrix, annot=True, cmap='coolwarm');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The correlation coefficient ranges from -1 to 1:\n",
    "* if the value is close to 1 there is a strong positive correlation between the two variables\n",
    "* if the value is close to -1, the variables have a strong negative correlation\n",
    "\n",
    "To fit a linear regression model, we select those features which have a **high correlation** with our target variable.<br>\n",
    "An important point in selecting features for a linear regression model is to **check for multi-co-linearity**.<br> \n",
    "\n",
    "By looking at the correlation matrix we can see that the variable that is most correlated with the target (which in this case is 'Brain Weight') is 'Head Size' (0.8). The other variables are not strongly correlated with the target variable, but perhaps they are worth including in the training model. It is good that the variables are not correlated with each other: we should avoid selecting the features that are (highly) correlated together."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using a scatter plot let’s see how these features vary with 'Brain Weight'."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1296x288 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Scatterplot of the features\n",
    "plt.figure(figsize=(18, 4))\n",
    "\n",
    "features = ['Gender', 'Age Range', 'Head Size']\n",
    "target = data['Brain Weight']\n",
    "\n",
    "for i, col in enumerate(features):\n",
    "    plt.subplot(1, len(features) , i+1)\n",
    "    x = data[col]\n",
    "    y = target\n",
    "    plt.scatter(x, y, marker='o')\n",
    "    plt.title(col)\n",
    "    plt.xlabel(col)\n",
    "    plt.ylabel('Target')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1296x288 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# It is better to consider the scatterplot only for continuous variables, \n",
    "# for categorical variables we consider a boxplot\n",
    "\n",
    "plt.figure(figsize=(18, 4))\n",
    "\n",
    "# Categorical\n",
    "plt.subplot(1, 3, 1)\n",
    "sns.boxplot(x=data['Gender'], y=target);\n",
    "plt.title('Gender');\n",
    "\n",
    "# Categorical\n",
    "plt.subplot(1, 3, 2)\n",
    "sns.boxplot(x=data['Age Range'], y=target);\n",
    "plt.title('Age Range');\n",
    "\n",
    "# Continuous\n",
    "plt.subplot(1, 3, 3)\n",
    "plt.scatter(x=data['Head Size'], y=target, marker='o');\n",
    "plt.title('Head Size');\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Insights**\n",
    "\n",
    "* It seems that gender can make some difference\n",
    "* The age range does not seem to have too much of an effect on its own (as even the confusion matrix suggests), perhaps, however, interaction with the other variables may give effect (as seen in the EDA notebook)\n",
    "* The brain weight increase as the value of the head size increases linearly (it looks to be following exactly a linear line)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.9.10 64-bit (system)",
   "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": "3.9.10"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "9805bccb659ffcb6952b8b29440036fac091f8170d71a5a402ad9a8f2988209f"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}