salary_prj2.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 124,
   "id": "6d409c99",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.metrics import mean_squared_error, r2_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "id": "065a0aa7",
   "metadata": {},
   "outputs": [],
   "source": [
    "def read_data():\n",
    "    data = pd.read_csv(\"D:/Topmentor/Oct/29th oct/b88assignments/Project - 1&2_ Simple_Reg/Project - 1&2_ Simple_Reg/Salary_Data.csv\")\n",
    "    return data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "id": "3cdfcc26",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   YearsExperience   Salary\n",
      "0              1.1  39343.0\n",
      "1              1.3  46205.0\n",
      "2              1.5  37731.0\n",
      "3              2.0  43525.0\n",
      "4              2.2  39891.0\n",
      "---------------------------------\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 30 entries, 0 to 29\n",
      "Data columns (total 2 columns):\n",
      " #   Column           Non-Null Count  Dtype  \n",
      "---  ------           --------------  -----  \n",
      " 0   YearsExperience  30 non-null     float64\n",
      " 1   Salary           30 non-null     float64\n",
      "dtypes: float64(2)\n",
      "memory usage: 612.0 bytes\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "df = read_data()\n",
    "print(df.head())\n",
    "print(\"---------------------------------\")\n",
    "print(df.info())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "id": "5c15ed04",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The scatter plot of Salary vs YearsExperience shows a generally positive linear relationship. As years of experience increase, salaries tend to rise.\n"
     ]
    }
   ],
   "source": [
    "# Scatter plot\n",
    "plt.scatter(df['YearsExperience'], df['Salary'])\n",
    "plt.title('Scatter Plot of Salary vs YearsExperience')\n",
    "plt.xlabel('YearsExperience')\n",
    "plt.ylabel('Salary')\n",
    "plt.show()\n",
    "print(\"The scatter plot of Salary vs YearsExperience shows a generally positive linear relationship. As years of experience increase, salaries tend to rise.\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "id": "97311f5f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split the data into training and testing sets\n",
    "X = df[['YearsExperience']]\n",
    "y = df['Salary']\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "id": "6cb01826",
   "metadata": {},
   "outputs": [],
   "source": [
    "def summary_statistics():\n",
    "    plt.figure(figsize = (10,3),edgecolor= 'blue')\n",
    "    sns.displot(df)\n",
    "    print(df.skew())\n",
    "    print(df.describe())\n",
    "    print(\"__________________________________\")\n",
    "    print(\"Summary statistics provide an overview of the data, including the mean, standard deviation, and quartiles for both variables.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "id": "b0b3a62a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "YearsExperience    0.37956\n",
      "Salary             0.35412\n",
      "dtype: float64\n",
      "       YearsExperience         Salary\n",
      "count        30.000000      30.000000\n",
      "mean          5.313333   76003.000000\n",
      "std           2.837888   27414.429785\n",
      "min           1.100000   37731.000000\n",
      "25%           3.200000   56720.750000\n",
      "50%           4.700000   65237.000000\n",
      "75%           7.700000  100544.750000\n",
      "max          10.500000  122391.000000\n",
      "__________________________________\n",
      "Summary statistics provide an overview of the data, including the mean, standard deviation, and quartiles for both variables.\n",
      "None\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1000x300 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 663.875x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sum_statistics = summary_statistics()\n",
    "print(sum_statistics)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e5d436f7",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "id": "0846ab2e",
   "metadata": {},
   "outputs": [],
   "source": [
    "def data_preprocessing_transformation():\n",
    "    print(df.isnull().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "id": "484ebddd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "YearsExperience    0\n",
      "Salary             0\n",
      "dtype: int64\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "dpt=data_preprocessing_transformation()\n",
    "print(dpt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "id": "a46b34c4",
   "metadata": {},
   "outputs": [],
   "source": [
    "## Now  Model Building\n",
    "def model_building():\n",
    "    df.shape\n",
    "    X= df['YearsExperience']   \n",
    "    y=df['Salary']\n",
    "    X_train,X_test,y_train,y_test = train_test_split(X,y,train_size=0.8)\n",
    "    X_train.shape,y_train.shape\n",
    "    X_test.shape,y_test.shape\n",
    "    X_train = X_train.values.reshape(-1,1)\n",
    "    y_train = y_train.values.reshape(-1,1)\n",
    "    \n",
    "    model = LinearRegression()\n",
    "    model_fit = model.fit(X_train,y_train)\n",
    "    coef= model_fit.coef_\n",
    "    intercept=model_fit.intercept_\n",
    "    \n",
    "    X_test = X_test.values.reshape(-1,1)\n",
    "    \n",
    "    y_pred = model_fit.predict(X_test)\n",
    "    print(np.round(y_pred))\n",
    "   \n",
    "    #Model evaluation\n",
    "    mse = mean_squared_error(y_test, y_pred)\n",
    "    r2 = r2_score(y_test, y_pred)\n",
    "    print(\"                     \")\n",
    "    print(\"Coeff:\",coef)\n",
    "    print(\"Interception:\", intercept)\n",
    "    print(f'Mean Squared Error: {mse:.2f}')\n",
    "    print(f'R-squared: {r2:.2f}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "id": "1ce7d197",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 63007.]\n",
      " [100309.]\n",
      " [ 74197.]\n",
      " [ 90051.]\n",
      " [ 47153.]\n",
      " [ 76062.]]\n",
      "                     \n",
      "Coeff: [[9325.64322564]]\n",
      "Interception: [26636.52243637]\n",
      "Mean Squared Error: 28782066.92\n",
      "R-squared: 0.93\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "dmb= model_building()\n",
    "print(dmb)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "id": "c6e6f554",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Coefficient:\n",
      "\n",
      "Coefficient: The coefficient of the model represents the estimated change in salary for a one-unit increase in years of experience. In this case, it indicates the average increase in salary for each additional year of experience.\n"
     ]
    }
   ],
   "source": [
    "print(f'Coefficient:')\n",
    "print(\"\\nCoefficient: The coefficient of the model represents the estimated change in salary for a one-unit increase in years of experience. In this case, it indicates the average increase in salary for each additional year of experience.\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "id": "74dd8103",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Intercept:\n",
      "\n",
      "The intercept represents the estimated starting salary when years of experience are zero. It contributes to the linear regression model.\n"
     ]
    }
   ],
   "source": [
    "\n",
    "print(f'Intercept:')\n",
    "print(\"\\nThe intercept represents the estimated starting salary when years of experience are zero. It contributes to the linear regression model.\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "id": "ec662953",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Squared Error: \n",
      "The mean squared error provides a measure of the average squared difference between actual and predicted salaries. A lower MSE indicates better model performance.\n"
     ]
    }
   ],
   "source": [
    "print(f'Mean Squared Error: ')\n",
    "print(\"The mean squared error provides a measure of the average squared difference between actual and predicted salaries. A lower MSE indicates better model performance.\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "id": "6c8a586f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R-squared: \n",
      "\n",
      "R-squared measures the proportion of the variance in the dependent variable (Salary) that is predictable from the independent variable (YearsExperience).\n",
      "It implies a strong correlation between the predictor and the response variable, indicating that the model does an excellent job of explaining the variability in the data\n"
     ]
    }
   ],
   "source": [
    "print(f'R-squared: ')\n",
    "print(\"\\nR-squared measures the proportion of the variance in the dependent variable (Salary) that is predictable from the independent variable (YearsExperience).\")\n",
    "print(\"It implies a strong correlation between the predictor and the response variable, indicating that the model does an excellent job of explaining the variability in the data\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "id": "7fe8e189",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Save pairplot\n",
    "plt.figure()\n",
    "sns.scatterplot(df)\n",
    "plt.savefig('D:/Topmentor/Oct/29th oct/Delivery project/salary_plot.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "id": "2b7b0d64",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Save pairplot\n",
    "plt.figure()\n",
    "plt.figure(figsize = (15,7))\n",
    "sns.barplot( data = df, x= 'YearsExperience', y='Salary',order=df['YearsExperience'].sort_values().unique(),palette=\"flare\")\n",
    "plt.savefig('D:/Topmentor/Oct/29th oct/Delivery project/Salary_bar.png')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b459dea4",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "67a77bc4",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "86dd8253",
   "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": "3.11.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}