Simple Linear Regression Model .ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "49378a9b",
   "metadata": {},
   "source": [
    "<div align=\"center\">\n",
    "    <h1>Prediction Model</h1>\n",
    "\n",
    "    \n",
    "    \n",
    "![Son's Height Prediction](https://static.vecteezy.com/system/resources/thumbnails/024/180/908/small_2x/smiling-father-measure-small-son-height-near-wall-happy-dad-check-little-boy-child-near-measurement-indoors-children-growing-illustration-vector.jpg)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eccb5f32",
   "metadata": {},
   "source": [
    "## Problem Statement: To Predict the Height of son by Father's height "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "c0681c37",
   "metadata": {},
   "outputs": [],
   "source": [
    "# import libraries\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import warnings\n",
    "warnings.filterwarnings('error')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "ffe39925",
   "metadata": {},
   "outputs": [],
   "source": [
    "# import Dataset\n",
    "\n",
    "fs = pd.read_csv('C:/Users/Win11/Documents/My learning/Python_Projects/Son_Father_height/Pearson.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "ab41238c",
   "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>Father</th>\n",
       "      <th>Son</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>65.0</td>\n",
       "      <td>59.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>63.3</td>\n",
       "      <td>63.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Father   Son\n",
       "0    65.0  59.8\n",
       "1    63.3  63.2"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fs.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "a58e951e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1078, 2)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fs.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "0a3fe231",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Father', 'Son'], dtype='object')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fs.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "c6d35c9d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<bound method DataFrame.info of       Father   Son\n",
       "0       65.0  59.8\n",
       "1       63.3  63.2\n",
       "2       65.0  63.3\n",
       "3       65.8  62.8\n",
       "4       61.1  64.3\n",
       "...      ...   ...\n",
       "1073    67.0  70.8\n",
       "1074    71.3  68.3\n",
       "1075    71.8  69.3\n",
       "1076    70.7  69.3\n",
       "1077    70.3  67.0\n",
       "\n",
       "[1078 rows x 2 columns]>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fs.info"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "db93ebdf",
   "metadata": {},
   "source": [
    "# Data Preprocessing "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "3c446c4d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Father    0\n",
       "Son       0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Checked the missing values\n",
    "\n",
    "fs.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "6a613f7e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "            Father          Son\n",
      "count  1078.000000  1078.000000\n",
      "mean     67.686827    68.684230\n",
      "std       2.745827     2.816194\n",
      "min      59.000000    58.500000\n",
      "25%      65.800000    66.900000\n",
      "50%      67.800000    68.600000\n",
      "75%      69.600000    70.500000\n",
      "max      75.400000    78.400000\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1078 entries, 0 to 1077\n",
      "Data columns (total 2 columns):\n",
      " #   Column  Non-Null Count  Dtype  \n",
      "---  ------  --------------  -----  \n",
      " 0   Father  1078 non-null   float64\n",
      " 1   Son     1078 non-null   float64\n",
      "dtypes: float64(2)\n",
      "memory usage: 17.0 KB\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "## Statiscal Description\n",
    "\n",
    "print(fs.describe())\n",
    "print(fs.info())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "3ffec777",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Father    4.177991e-12\n",
      "Son      -1.421085e-12\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# Count outliers\n",
    "    \n",
    "from scipy import stats\n",
    "z_scores = stats.zscore(fs[['Father', 'Son']])\n",
    "print((z_scores).sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "e67eff20",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "68.6    26\n",
       "68.1    21\n",
       "68.2    21\n",
       "67.2    20\n",
       "66.3    20\n",
       "        ..\n",
       "61.3     1\n",
       "75.3     1\n",
       "61.8     1\n",
       "62.2     1\n",
       "58.8     1\n",
       "Name: Son, Length: 146, dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fs.Father.value_counts()\n",
    "fs.Son.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "492055f9",
   "metadata": {},
   "outputs": [],
   "source": [
    "n_unique_father = fs['Father'].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "02d5d53a",
   "metadata": {},
   "outputs": [],
   "source": [
    "n_unique_Son = fs['Son'].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "67609ba5",
   "metadata": {},
   "outputs": [],
   "source": [
    "correlation = fs.corr()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9fa5ce7d",
   "metadata": {},
   "source": [
    "## Exploratory Data Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "b7888d18",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<AxesSubplot:title={'center':'Father'}>,\n",
       "        <AxesSubplot:title={'center':'Son'}>]], dtype=object)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Distribution of father and sons height\n",
    "\n",
    "fs.hist(column= ['Father','Son'], bins =10, figsize =(10,8))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "2870a35d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fs[['Father', 'Son']].plot(kind='box', figsize=(8, 6))\n",
    "plt.title(\"Box Plot of Father and Son Heights\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "4eabd2a7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Correlation Analysis\n",
    "\n",
    "sns.heatmap(data = correlation, annot= True, cmap = 'coolwarm' )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "2c4126f4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Bivariate Analysis\n",
    "\n",
    "plt.scatter(fs['Father'], fs['Son'], color='blue', linewidths=None)\n",
    "plt.title('Father vs Son Heights')\n",
    "plt.xlabel('Father Height')\n",
    "plt.ylabel('Son Height')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "a9056fad",
   "metadata": {},
   "outputs": [],
   "source": [
    "# importing libraries to SLR Build Model \n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LinearRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "4fa0c0c3",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = fs[['Father']]  # Independent variable (feature)\n",
    "y = fs['Son']  # Dependent variable (target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "560820a0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Splitting Dataset \n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "f9d5482a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Model fit\n",
    "\n",
    "model = LinearRegression()\n",
    "model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "f104aa9b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      Father\n",
      "239     63.9\n",
      "1016    70.6\n",
      "725     66.1\n",
      "486     66.0\n",
      "561     71.1\n",
      "...      ...\n",
      "474     64.7\n",
      "632     68.8\n",
      "658     63.0\n",
      "533     72.4\n",
      "483     66.1\n",
      "\n",
      "[216 rows x 1 columns]\n"
     ]
    }
   ],
   "source": [
    "print(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "a7d77ca1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "239     66.7\n",
      "1016    70.4\n",
      "725     68.1\n",
      "486     67.1\n",
      "561     69.5\n",
      "        ... \n",
      "474     66.8\n",
      "632     75.7\n",
      "658     65.2\n",
      "533     68.6\n",
      "483     67.2\n",
      "Name: Son, Length: 216, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print(y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "bdd582e4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Slope: 0.5095715884190017\n",
      "Intercept: 34.23079977950441\n"
     ]
    }
   ],
   "source": [
    "# slope, intercept \n",
    "\n",
    "Slope = model.coef_[0]       \n",
    "intercept = model.intercept_\n",
    "print('Slope:', Slope)\n",
    "print('Intercept:', intercept)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "ba28c392",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Y = Mx + C\n",
    "\n",
    "Predicted_Son = 0.5095715884190017 * 63.9 + 34.23079977950441"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "0ba00183",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "169d454f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of y: (1078,)\n",
      "Shape of y_pred: (216,)\n"
     ]
    }
   ],
   "source": [
    "print(\"Shape of y:\", y.shape)\n",
    "print(\"Shape of y_pred:\", y_pred.shape)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "0e899ad2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Squared Error (MSE): 6.117228348454616\n",
      "Root Mean Squared Error (RMSE): 2.4733031250646604\n",
      "Mean Absolute Error (MAE): 1.9075737297322102\n",
      "R-squared (R^2): 0.24422017915747418\n"
     ]
    }
   ],
   "source": [
    "# Model Evaluation  \n",
    "\n",
    "from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score\n",
    "\n",
    "mse = mean_squared_error(y_test, y_pred)\n",
    "rmse = mean_squared_error(y_test, y_pred, squared=False)\n",
    "mae = mean_absolute_error(y_test, y_pred)\n",
    "r2 = r2_score(y_test, y_pred)\n",
    "\n",
    "print('Mean Squared Error (MSE):', mse)\n",
    "print('Root Mean Squared Error (RMSE):', rmse)\n",
    "print('Mean Absolute Error (MAE):', mae)\n",
    "print('R-squared (R^2):', r2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "afac25dd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(X_test, y_test, color='blue', label='Actual data')\n",
    "plt.plot(X_test, y_pred, color='red', linewidth=2, label='Regression Line')\n",
    "\n",
    "plt.title('Linear Regression: Father vs Son Heights')\n",
    "plt.xlabel('Father Height')\n",
    "plt.ylabel('Son Height')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9a817200",
   "metadata": {},
   "source": [
    "## Summary \n",
    "\n",
    "Based on the evaluation metrics provided, here is a summary of my linear regression model's performance:\n",
    "\n",
    "Model Performance Summary\n",
    "\n",
    "- Mean Squared Error (MSE): 6.12\n",
    "- Root Mean Squared Error (RMSE): 2.47\n",
    "- Mean Absolute Error (MAE): 1.91\n",
    "- R-squared (R²): 0.24\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "21d1104e",
   "metadata": {},
   "source": [
    "# Insights"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e958342e",
   "metadata": {},
   "source": [
    "## Correlation Strength: \n",
    "\n",
    "There is a significant positive correlation between the heights of fathers and sons, as evidenced by the scatter plot and correlation matrix. This indicates that taller fathers tend to have taller sons, and this relationship is linear enough to model with linear regression.\n",
    "\n",
    "## Model Effectiveness:\n",
    "\n",
    "The slope of the regression line (coefficient for the father's height) indicates the average increase in the son's height for each unit increase in the father's height. The positive slope value confirms the direct relationship\n",
    "\n",
    "the intercept, representing the son's height when the father's height is zero, serves primarily as a statistical parameter and has less practical interpretability in this context\n",
    "\n",
    "## Statistical Measures:\n",
    "\n",
    "The Mean Squared Error (MSE) and Root Mean Squared Error (RMSE) provide measures of the average error magnitude in the predictions. The values indicate how closely the regression line fits the data points overall.\n",
    "\n",
    "The Mean Absolute Error (MAE) gives an average of the absolute errors, providing a clearer picture of typical prediction errors without squaring, thus not overly penalizing large errors as MSE and RMSE do.\n",
    "\n",
    "The R-squared (R²) value, representing the proportion of variance in the son's height that is predictable from the father's height, suggests a moderate to strong fit of the model to the data.\n",
    "\n",
    "## Visualization Impact:\n",
    "\n",
    "The final plot combining the actual data points with the regression line visually underscores the relationship's strength and the model's fit, demonstrating that the model captures the trend in the data effectively."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "388d91c1",
   "metadata": {},
   "source": [
    "## Recommendations"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0b4c9f29",
   "metadata": {},
   "source": [
    "## Practical Application:\n",
    "The model's insights can be useful in pediatric growth tracking, genetic studies, and healthcare to predict growth trends and assess deviations from expected growth patterns"
   ]
  }
 ],
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