Sales_Prediction_Using_Python.ipynb

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    "language_info": {
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  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# **🙋**"
      ],
      "metadata": {
        "id": "RmRalNUHXtY6"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **OASIS INFOBYTE**"
      ],
      "metadata": {
        "id": "iCGFowoxXqQu"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Data Science Internship**"
      ],
      "metadata": {
        "id": "Z4hytqtrX7GD"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Author: Ahmad Talha Ansari**"
      ],
      "metadata": {
        "id": "9x1vrfd1X_sG"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Sales Prediction using Python**"
      ],
      "metadata": {
        "id": "QXQXxDa1xZ4F"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Importing Necessary Libraries**"
      ],
      "metadata": {
        "id": "ZNxKm9XVUjwL"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 49,
      "metadata": {
        "id": "68hx1iG1xSjC"
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      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.linear_model import LinearRegression, SGDRegressor\n",
        "from sklearn.tree import DecisionTreeRegressor\n",
        "from sklearn.ensemble import RandomForestRegressor\n",
        "from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Loading Dataset**"
      ],
      "metadata": {
        "id": "ETtLz9gHUtTX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv(\"Advertising.csv\")\n",
        "df.head()"
      ],
      "metadata": {
        "id": "X_0GNBmFxr2m",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "9e7ba9e3-df8e-4965-e9ea-eb37448d6b1f"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Unnamed: 0     TV  Radio  Newspaper  Sales\n",
              "0           1  230.1   37.8       69.2   22.1\n",
              "1           2   44.5   39.3       45.1   10.4\n",
              "2           3   17.2   45.9       69.3    9.3\n",
              "3           4  151.5   41.3       58.5   18.5\n",
              "4           5  180.8   10.8       58.4   12.9"
            ],
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          },
          "metadata": {},
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        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Know the Data...**"
      ],
      "metadata": {
        "id": "Sgb0dPVtUzVh"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# shape of dataset\n",
        "df.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RxDqnv9T0o1h",
        "outputId": "e12ab502-0f40-473b-98a5-6961113f026b"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(200, 5)"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# no. of null rows in dataset\n",
        "df.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "j-Br5KriyWw8",
        "outputId": "2cd5e933-12a1-48cf-be27-5bfec3fad596"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Unnamed: 0    0\n",
              "TV            0\n",
              "Radio         0\n",
              "Newspaper     0\n",
              "Sales         0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# no. of duplicated rows in dataset\n",
        "df.duplicated().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RDQRnhLIymwX",
        "outputId": "c816e864-7dfa-4634-f0ef-0d3d9c66e8af"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Data Cleaning**"
      ],
      "metadata": {
        "id": "LiPzEMtJVFQZ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# dropping unwanted columns\n",
        "df.drop(columns = [\"Unnamed: 0\"], inplace=True)"
      ],
      "metadata": {
        "id": "Hx6ZRAIWytW3"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **EDA**"
      ],
      "metadata": {
        "id": "yLLDk_CnVTfx"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sns.heatmap(df.iloc[:, :-1].corr(), annot=True)\n",
        "plt.plot()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 452
        },
        "id": "60vWm3aQy_ni",
        "outputId": "73b7d3ae-ec74-4795-87a4-34734c413bb2"
      },
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[]"
            ]
          },
          "metadata": {},
          "execution_count": 61
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Splitting Data into Features and Target**"
      ],
      "metadata": {
        "id": "qWK-_WUAYLmA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "features = df.iloc[:, :-1]\n",
        "y = df.iloc[:, -1]"
      ],
      "metadata": {
        "id": "yl6fdO_f2Ib4"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Plotting the Relationship 📈 between Features and Target**"
      ],
      "metadata": {
        "id": "zO4AbUIXYTjH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(8, 3))\n",
        "plt.subplot(1, 3, 1)\n",
        "plt.scatter(features.iloc[:, 0], y)\n",
        "plt.xlabel(\"TV\")\n",
        "plt.ylabel(\"Sales\")\n",
        "\n",
        "plt.subplot(1, 3, 2)\n",
        "plt.scatter(features.iloc[:, 1], y)\n",
        "plt.xlabel(\"Radio\")\n",
        "plt.ylabel(\"Sales\")\n",
        "\n",
        "plt.subplot(1, 3, 3)\n",
        "plt.scatter(features.iloc[:, 2], y)\n",
        "plt.xlabel(\"Newspaper\")\n",
        "plt.ylabel(\"Sales\")\n",
        "\n",
        "plt.tight_layout()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 307
        },
        "id": "8RlGlzjCUCtU",
        "outputId": "4e3e7a11-f24b-4fe8-c8e5-2055ae73acc5"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x300 with 3 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Splitting Data into Training and Testing**"
      ],
      "metadata": {
        "id": "ShmaH3awYlsp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(features.iloc[:, :], y, random_state=0, test_size=0.2)"
      ],
      "metadata": {
        "id": "maezzUa5WrcM"
      },
      "execution_count": 45,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Model Building & Predictions**"
      ],
      "metadata": {
        "id": "qYmkXzEuYqu9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "rf = RandomForestRegressor().fit(X_train, y_train)\n",
        "y_pred = rf.predict(X_test)"
      ],
      "metadata": {
        "id": "oYX_RsTBXP0p"
      },
      "execution_count": 59,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Regression Metrics**"
      ],
      "metadata": {
        "id": "nsqdU6-bY2O_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "mae = mean_absolute_error(y_test, y_pred)\n",
        "mse = mean_squared_error(y_test, y_pred)\n",
        "rmse = np.sqrt(mean_squared_error(y_test, y_pred))\n",
        "r2 = r2_score(y_test, y_pred)\n",
        "\n",
        "print(f\"\"\"\n",
        "        mean absolute error:     {mae},\n",
        "        mean squared error :     {mse},\n",
        "        root mean squared error: {rmse},\n",
        "        r2 score:                {r2}\n",
        "      \"\"\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_IOuBrO9Yxw8",
        "outputId": "595be20e-ec67-41a3-b131-4391cb1a3b32"
      },
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "        mean absolute error:     0.6777500000000009,\n",
            "        mean squared error :     0.9029364999999986,\n",
            "        root mean squared error: 0.9502297090703903,\n",
            "        r2 score:                0.9713075163586264 \n",
            "      \n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **♥**\n"
      ],
      "metadata": {
        "id": "KpjGrnDubB-7"
      }
    }
  ]
}