Delivery_prj1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "80561792",
   "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": 4,
   "id": "1568afa1",
   "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/delivery_time.csv\")\n",
    "    return data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "56d79d63",
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Delivery Time  Sorting Time\n",
      "0          21.00            10\n",
      "1          13.50             4\n",
      "2          19.75             6\n",
      "3          24.00             9\n",
      "4          29.00            10\n",
      "---------------------------------\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 21 entries, 0 to 20\n",
      "Data columns (total 2 columns):\n",
      " #   Column         Non-Null Count  Dtype  \n",
      "---  ------         --------------  -----  \n",
      " 0   Delivery Time  21 non-null     float64\n",
      " 1   Sorting Time   21 non-null     int64  \n",
      "dtypes: float64(1), int64(1)\n",
      "memory usage: 468.0 bytes\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "df = read_data()\n",
    "print(df.head())\n",
    "print(\"---------------------------------\")\n",
    "print(df.info())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "cd64b736",
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Delivery Time  Sorting Time\n",
      "0           21.00            10\n",
      "1           13.50             4\n",
      "2           19.75             6\n",
      "3           24.00             9\n",
      "4           29.00            10\n",
      "5           15.35             6\n",
      "6           19.00             7\n",
      "7            9.50             3\n",
      "8           17.90            10\n",
      "9           18.75             9\n",
      "10          19.83             8\n",
      "11          10.75             4\n",
      "12          16.68             7\n",
      "13          11.50             3\n",
      "14          12.03             3\n",
      "15          14.88             4\n",
      "16          13.75             6\n",
      "17          18.11             7\n",
      "18           8.00             2\n",
      "19          17.83             7\n",
      "20          21.50             5\n"
     ]
    }
   ],
   "source": [
    "df_copy = df.copy()\n",
    "print(df_copy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "bf9d6d7b",
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7     4\n",
       "10    3\n",
       "4     3\n",
       "6     3\n",
       "3     3\n",
       "9     2\n",
       "8     1\n",
       "2     1\n",
       "5     1\n",
       "Name: Sorting Time, dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_copy['Sorting Time'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "b1e144e0",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_sort = df_copy.sort_values(by='Sorting Time')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "7cc8e335",
   "metadata": {},
   "outputs": [],
   "source": [
    "def exploratory_data_analysis():\n",
    "    plt.figure(figsize = (8,5))\n",
    "    sns.scatterplot( data = df_copy, x= 'Sorting Time', y='Delivery Time')\n",
    "    plt.title(\"Scatter Plot of Sorting Time vs. Delivery Time\")\n",
    "    plt.show()\n",
    "    print(\"Insights/Inference:\")\n",
    "    print(\"1.The dataset contains two columns, 'Delivery Time' and 'Sorting Time.'\")\n",
    "    print(\"2.From the scatter plot, it appears there is a positive linear relationship between Sorting Time and Delivery Time. As Sorting Time increases, Delivery Time tends to increase.\")\n",
    "    plt.savefig('D:/Topmentor/Oct/29th oct/Delivery project/delivery_scat.png')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "e0106da3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Insights/Inference:\n",
      "1.The dataset contains two columns, 'Delivery Time' and 'Sorting Time.'\n",
      "2.From the scatter plot, it appears there is a positive linear relationship between Sorting Time and Delivery Time. As Sorting Time increases, Delivery Time tends to increase.\n",
      "None\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "eda = exploratory_data_analysis()\n",
    "print(eda)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "a3c99a2a",
   "metadata": {},
   "outputs": [],
   "source": [
    "def summary_statistics():\n",
    "    plt.figure(figsize = (10,3),edgecolor= 'blue')\n",
    "    sns.displot(df_copy)\n",
    "    print(df_copy.skew())\n",
    "    print(df_copy.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": 12,
   "id": "41bbe368",
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Delivery Time    0.352390\n",
      "Sorting Time     0.047115\n",
      "dtype: float64\n",
      "       Delivery Time  Sorting Time\n",
      "count      21.000000     21.000000\n",
      "mean       16.790952      6.190476\n",
      "std         5.074901      2.542028\n",
      "min         8.000000      2.000000\n",
      "25%        13.500000      4.000000\n",
      "50%        17.830000      6.000000\n",
      "75%        19.750000      8.000000\n",
      "max        29.000000     10.000000\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 646.25x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sum_statistics = summary_statistics()\n",
    "print(sum_statistics)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "26578e77",
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Correlation Heatmap\n",
    "correlation_matrix = df_copy.corr()\n",
    "sns.heatmap(correlation_matrix, annot=True, cmap=\"coolwarm\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "dc3c9253",
   "metadata": {},
   "outputs": [],
   "source": [
    "def data_preprocessing_transformation():\n",
    "    print(df_copy.isnull().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "d8a47b96",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Delivery Time    0\n",
      "Sorting Time     0\n",
      "dtype: int64\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "dpt=data_preprocessing_transformation()\n",
    "print(dpt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "7ea979ae",
   "metadata": {},
   "outputs": [],
   "source": [
    "## Now  Model Building\n",
    "def model_building():\n",
    "    df_copy.shape\n",
    "    X= df_copy['Sorting Time']   \n",
    "    y=df_copy['Delivery Time']\n",
    "    X_train,X_test,y_train,y_test = train_test_split(X,y,train_size=0.2,random_state=42)\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(\"The Predicted values:\\n\", np.round(y_pred,2))\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(\"Coefficients:\", np.round(coef,2))\n",
    "    print(\"Intercept:\",np.round(intercept,2))\n",
    "    print(f'Mean Squared Error: {mse:.2f}')\n",
    "    print(f'R-squared: {r2:.2f}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "47f6fe49",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The Predicted values:\n",
      " [[23.08]\n",
      " [18.35]\n",
      " [13.63]\n",
      " [13.63]\n",
      " [23.08]\n",
      " [16.78]\n",
      " [13.63]\n",
      " [21.5 ]\n",
      " [10.48]\n",
      " [16.78]\n",
      " [12.05]\n",
      " [16.78]\n",
      " [21.5 ]\n",
      " [15.2 ]\n",
      " [23.08]\n",
      " [18.35]\n",
      " [12.05]]\n",
      "                     \n",
      "Coefficients: [[1.57]]\n",
      "Intercept: [7.33]\n",
      "Mean Squared Error: 9.73\n",
      "R-squared: 0.65\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "dmb= model_building()\n",
    "print(dmb)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "ae17a10f",
   "metadata": {
    "scrolled": true
   },
   "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_copy, x= 'Delivery Time', y='Sorting Time',palette=\"flare\")\n",
    "plt.savefig('D:/Topmentor/Oct/29th oct/Delivery project/delivery_pairplot.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "625ea3ff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Save correlation heatmap\n",
    "plt.figure()\n",
    "sns.heatmap(correlation_matrix, annot=True, cmap=\"coolwarm\")\n",
    "plt.savefig('D:/Topmentor/Oct/29th oct/Delivery project/delivery_correlation_heatmap.png')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bcb7199d",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "073b3337",
   "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
}