project1_salary_hike.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "d5083f36-e799-4200-850b-f5d41f003345",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Import the libraries Successfully\n",
      "Reading of Data\n",
      "   YearsExperience  Salary\n",
      "0              1.1   39343\n",
      "1              1.3   46205\n",
      "number of records- 30\n",
      "columns- Index(['YearsExperience', 'Salary'], dtype='object')\n",
      "missing-\n",
      " YearsExperience    0\n",
      "Salary             0\n",
      "dtype: int64\n",
      "YearsExperience    float64\n",
      "Salary               int64\n",
      "dtype: object\n",
      "<bound method NDFrame.describe of     YearsExperience  Salary\n",
      "0               1.1   39343\n",
      "1               1.3   46205\n",
      "2               1.5   37731\n",
      "3               2.0   43525\n",
      "4               2.2   39891\n",
      "5               2.9   56642\n",
      "6               3.0   60150\n",
      "7               3.2   54445\n",
      "8               3.2   64445\n",
      "9               3.7   57189\n",
      "10              3.9   63218\n",
      "11              4.0   55794\n",
      "12              4.0   56957\n",
      "13              4.1   57081\n",
      "14              4.5   61111\n",
      "15              4.9   67938\n",
      "16              5.1   66029\n",
      "17              5.3   83088\n",
      "18              5.9   81363\n",
      "19              6.0   93940\n",
      "20              6.8   91738\n",
      "21              7.1   98273\n",
      "22              7.9  101302\n",
      "23              8.2  113812\n",
      "24              8.7  109431\n",
      "25              9.0  105582\n",
      "26              9.5  116969\n",
      "27              9.6  112635\n",
      "28             10.3  122391\n",
      "29             10.5  121872>\n",
      "Salary_skewness 0.35411967922959153\n",
      "YearsExperience_skewness 0.37956024064804106\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 700x500 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "YearsExperience    8.053609e+00\n",
      "Salary             7.515510e+08\n",
      "dtype: float64\n",
      "training is done-\n",
      "Score for training model 0.950740977857383\n",
      "Prediction-\n",
      " [[ 55873.15231244]\n",
      " [ 62539.44008222]\n",
      " [ 46349.88406989]\n",
      " [ 37778.94265159]\n",
      " [123488.35683453]]\n",
      "Final score- 0.9734912916857618\n",
      "Total time taken in second - 1.439150094985962\n"
     ]
    }
   ],
   "source": [
    "##importing libraries\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import time\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import r2_score\n",
    "print(\"Import the libraries Successfully\")\n",
    "st = time.time()\n",
    "\n",
    "## reading Data\n",
    "print(\"Reading of Data\")\n",
    "def read_data():\n",
    "    try:\n",
    "       data=pd.read_csv(\"E:/Salary_Data.csv\")\n",
    "       print(data.head(2))\n",
    "       return data\n",
    "    except Exception as e:\n",
    "       print(\"Error in read data function_\",e)\n",
    "##call the function\n",
    "data=read_data()\n",
    "\n",
    "## data profiling\n",
    "def data_profiling(data):\n",
    "    try:\n",
    "        records=len(data)\n",
    "        columns=data.columns\n",
    "        missing=data.isnull().sum()\n",
    "        data_types=data.dtypes\n",
    "        stats=data.describe\n",
    "        print(\"number of records-\",records)\n",
    "        print(\"columns-\",columns)\n",
    "        print(\"missing-\\n\",missing)\n",
    "        print(data_types)\n",
    "        print(stats)\n",
    "    except Exception as e:\n",
    "        print(\"Error in data profile function-\",e)\n",
    "#calling function\n",
    "data_profiling(data)\n",
    "\n",
    "##Exploratory data Analysis\n",
    "def exploratory_analysis(data):\n",
    "    try:\n",
    "        plt.figure(figsize=(7,5))\n",
    "        ax=sns.displot(x=data['YearsExperience'])\n",
    "        plt.figure()\n",
    "        ax1=sns.displot(x=data['Salary'])\n",
    "        print(\"Salary_skewness\",data['Salary'].skew())\n",
    "        print(\"YearsExperience_skewness\",data['YearsExperience'].skew())\n",
    "        corr=data.corr()\n",
    "        plt.figure()\n",
    "        ax3=sns.heatmap(corr,annot=True)\n",
    "        plt.show()\n",
    "        var=data.var()\n",
    "        print(var)\n",
    "    except Exception as e:\n",
    "        print(\"Error in exploratory analysis-\",e)\n",
    "#calling function\n",
    "exploratory_analysis(data)\n",
    "\n",
    "##data splitting\n",
    "\n",
    "def data_splitting(data):\n",
    "    try:\n",
    "        X=data['YearsExperience'].values.reshape(-1,1)\n",
    "        y=data['Salary'].values.reshape(-1,1)\n",
    "\n",
    "#splitting of data\n",
    "        X_train,X_test,y_train,y_test = train_test_split(X,y,test_size=0.15, random_state=1234)\n",
    "        return X_train,X_test,y_train,y_test\n",
    "    except Exception as e:\n",
    "        print(\"Errors in data splitting-\",e)\n",
    "X_train,X_test,y_train,y_test=data_splitting(data)    \n",
    "\n",
    "##data modelling\n",
    "def data_modelling(X_train, y_train):\n",
    "    global model\n",
    "    model = LinearRegression()\n",
    "    model.fit(X_train, y_train)\n",
    "    print(\"training is done-\")\n",
    "    coeff=model.coef_\n",
    "    constant=model.intercept_\n",
    "    score=model.score(X_train,y_train)\n",
    "    print(\"Score for training model\",score)\n",
    "    return coeff,constant\n",
    "#calling function\n",
    "coeff,constant=data_modelling(X_train, y_train)    \n",
    "\n",
    "##prediction and evaluation on predicted data\n",
    "def model_evaluation(X_test, y_test):\n",
    "    predict= model.predict(X_test)\n",
    "    final_score =r2_score(y_test,predict)\n",
    "    return predict,final_score\n",
    "predict,final_score=model_evaluation(X_test, y_test)\n",
    "print(\"Prediction-\\n\",predict)\n",
    "print(\"Final score-\",final_score)\n",
    "\n",
    "en=time.time()\n",
    "print(\"Total time taken in second -\",en-st)\n",
    "\n",
    "\n",
    "        \n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "00aed001-4b7f-4ebf-8a47-3d77581bbd02",
   "metadata": {},
   "source": [
    "## Exploratory Data Analysis and Interprete with Graphs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "5dbb1c11-b918-4778-a841-2adae38d7305",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 0 Axes>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 700x500 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "plt.figure(figsize=(7,5))\n",
    "ax=sns.displot(x=data['YearsExperience'])\n",
    "plt.figure()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "7acf86a5-53c0-4b43-99b3-abcfab5089cc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "        ax1=sns.displot(x=data['Salary'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "70751041-4d07-4894-9955-5940717afd2a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "        corr=data.corr()\n",
    "        plt.figure()\n",
    "        ax3=sns.heatmap(corr,annot=True)\n",
    "        plt.show()\n",
    "        \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3916e13b-b844-48c7-af96-a61db208004a",
   "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": "3.12.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}