Inventory_Management.ipynb

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  "metadata": {
    "colab": {
      "provenance": [],
      "mount_file_id": "14V563y1sdmXIriM-V88_ipTPML3rgvDG",
      "authorship_tag": "ABX9TyOr7zjzpjAQp/Dwk1x+uXoX",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Sara-Esm/Inventory_Management/blob/main/Inventory_Management.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Requirements and Setup"
      ],
      "metadata": {
        "id": "_pyW9RFlXsgK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Install libraries\n",
        "!pip install scikit-learn seaborn joblib\n",
        "\n",
        "# Import required libraries\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.ensemble import RandomForestRegressor\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import mean_squared_error, r2_score\n",
        "import joblib"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "C1AlFT5f__VO",
        "outputId": "b4c1d5e0-9d96-4693-8e08-84c1f14aac2a"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: scikit-learn in /usr/local/lib/python3.10/dist-packages (1.5.2)\n",
            "Requirement already satisfied: seaborn in /usr/local/lib/python3.10/dist-packages (0.13.2)\n",
            "Requirement already satisfied: joblib in /usr/local/lib/python3.10/dist-packages (1.4.2)\n",
            "Requirement already satisfied: numpy>=1.19.5 in /usr/local/lib/python3.10/dist-packages (from scikit-learn) (1.26.4)\n",
            "Requirement already satisfied: scipy>=1.6.0 in /usr/local/lib/python3.10/dist-packages (from scikit-learn) (1.13.1)\n",
            "Requirement already satisfied: threadpoolctl>=3.1.0 in /usr/local/lib/python3.10/dist-packages (from scikit-learn) (3.5.0)\n",
            "Requirement already satisfied: pandas>=1.2 in /usr/local/lib/python3.10/dist-packages (from seaborn) (2.2.2)\n",
            "Requirement already satisfied: matplotlib!=3.6.1,>=3.4 in /usr/local/lib/python3.10/dist-packages (from seaborn) (3.7.1)\n",
            "Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (1.3.0)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.10/dist-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (0.12.1)\n",
            "Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (4.54.1)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (1.4.7)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (24.1)\n",
            "Requirement already satisfied: pillow>=6.2.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (10.4.0)\n",
            "Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (3.2.0)\n",
            "Requirement already satisfied: python-dateutil>=2.7 in /usr/local/lib/python3.10/dist-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas>=1.2->seaborn) (2024.2)\n",
            "Requirement already satisfied: tzdata>=2022.7 in /usr/local/lib/python3.10/dist-packages (from pandas>=1.2->seaborn) (2024.2)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.7->matplotlib!=3.6.1,>=3.4->seaborn) (1.16.0)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Data Loading and Exploration"
      ],
      "metadata": {
        "id": "NjkXQ3BCYEjV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Load the dataset\n",
        "df = pd.read_csv('/walmart-recruiting-store-sales-forecasting-privateleaderboard-2024-10-20T00_53_20.csv')\n",
        "\n",
        "# Check the first few rows of the data\n",
        "print(\"First five rows of the dataset:\")\n",
        "print(df.head())\n",
        "\n",
        "# Check for missing values and data types\n",
        "print(\"Missing values in each column:\")\n",
        "print(df.isnull().sum())\n",
        "print(\"Data types of each column:\")\n",
        "print(df.dtypes)\n",
        "\n",
        "# Basic statistics of the dataset\n",
        "print(\"Statistical summary:\")\n",
        "print(df.describe())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "b3mi4zlcZ8Na",
        "outputId": "f683c272-f987-4bed-a5cf-59b60043e8bb"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "First five rows of the dataset:\n",
            "   Rank  TeamId            TeamName   LastSubmissionDate       Score  \\\n",
            "0     1   79642        David Thaler  2014-05-05 22:59:41  2301.48792   \n",
            "1     2   81034  Srihari Jaganathan  2014-05-05 19:33:55  2371.42364   \n",
            "2     3   84030          James King  2014-05-04 00:31:57  2394.70198   \n",
            "3     4   82827              Giulio  2014-04-28 17:41:57  2424.24309   \n",
            "4     5   80191           Domcastro  2014-05-05 13:13:04  2427.05483   \n",
            "\n",
            "   SubmissionCount TeamMemberUserNames  \n",
            "0              223         davidthaler  \n",
            "1              211            sriharij  \n",
            "2              170           jfkingiii  \n",
            "3              104           adjgiulio  \n",
            "4              216           domcastro  \n",
            "Missing values in each column:\n",
            "Rank                   0\n",
            "TeamId                 0\n",
            "TeamName               0\n",
            "LastSubmissionDate     0\n",
            "Score                  0\n",
            "SubmissionCount        0\n",
            "TeamMemberUserNames    0\n",
            "dtype: int64\n",
            "Data types of each column:\n",
            "Rank                     int64\n",
            "TeamId                   int64\n",
            "TeamName                object\n",
            "LastSubmissionDate      object\n",
            "Score                  float64\n",
            "SubmissionCount          int64\n",
            "TeamMemberUserNames     object\n",
            "dtype: object\n",
            "Statistical summary:\n",
            "             Rank        TeamId         Score  SubmissionCount\n",
            "count  689.000000    689.000000  6.890000e+02       689.000000\n",
            "mean   344.000000  84531.243832  4.882306e+10        17.766328\n",
            "std    199.041453   4524.231553  1.281547e+12        29.278668\n",
            "min      0.000000  74875.000000  2.301488e+03         1.000000\n",
            "25%    172.000000  80301.000000  3.028984e+03         3.000000\n",
            "50%    344.000000  84036.000000  3.800980e+03         8.000000\n",
            "75%    516.000000  88716.000000  1.005133e+04        19.000000\n",
            "max    688.000000  93144.000000  3.363908e+13       225.000000\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Data Preprocessing"
      ],
      "metadata": {
        "id": "oo6DegD6YOAl"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Fill Missing Values\n",
        "# Separate numeric and non-numeric columns\n",
        "numeric_cols = df.select_dtypes(include=[np.number]).columns\n",
        "non_numeric_cols = df.select_dtypes(exclude=[np.number]).columns\n",
        "\n",
        "# Fill missing values for numeric columns with mean\n",
        "df[numeric_cols] = df[numeric_cols].fillna(df[numeric_cols].mean())\n",
        "\n",
        "# Fill missing values for non-numeric (categorical) columns with mode\n",
        "for col in non_numeric_cols:\n",
        "    df[col] = df[col].fillna(df[col].mode()[0])\n",
        "\n",
        "print(\"Missing values filled. Updated data:\")\n",
        "print(df.head())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fM0Jz6SjiFAd",
        "outputId": "2b17fd9f-cf95-40f1-fece-0dd8d5313665"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Missing values filled. Updated data:\n",
            "   Rank  TeamId            TeamName   LastSubmissionDate       Score  \\\n",
            "0     1   79642        David Thaler  2014-05-05 22:59:41  2301.48792   \n",
            "1     2   81034  Srihari Jaganathan  2014-05-05 19:33:55  2371.42364   \n",
            "2     3   84030          James King  2014-05-04 00:31:57  2394.70198   \n",
            "3     4   82827              Giulio  2014-04-28 17:41:57  2424.24309   \n",
            "4     5   80191           Domcastro  2014-05-05 13:13:04  2427.05483   \n",
            "\n",
            "   SubmissionCount TeamMemberUserNames  \n",
            "0              223         davidthaler  \n",
            "1              211            sriharij  \n",
            "2              170           jfkingiii  \n",
            "3              104           adjgiulio  \n",
            "4              216           domcastro  \n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Feature Engineering"
      ],
      "metadata": {
        "id": "Os7VaQneYUYx"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Feature Engineering for Date column (e.g., LastSubmissionDate)\n",
        "if 'LastSubmissionDate' in df.columns:\n",
        "    df['LastSubmissionDate'] = pd.to_datetime(df['LastSubmissionDate'])\n",
        "    df['Year'] = df['LastSubmissionDate'].dt.year\n",
        "    df['Month'] = df['LastSubmissionDate'].dt.month\n",
        "    df['Day'] = df['LastSubmissionDate'].dt.day\n",
        "    df.drop(columns=['LastSubmissionDate'], inplace=True)  # Drop original date column\n",
        "else:\n",
        "    print(\"'LastSubmissionDate' column not found.\")\n",
        "\n",
        "print(\"Feature engineering completed. Updated data:\")\n",
        "print(df.head())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JaytLLV2j6zh",
        "outputId": "bcaf4143-afd2-493c-9dba-62c9fb08ca27"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Feature engineering completed. Updated data:\n",
            "   Rank  TeamId            TeamName       Score  SubmissionCount  \\\n",
            "0     1   79642        David Thaler  2301.48792              223   \n",
            "1     2   81034  Srihari Jaganathan  2371.42364              211   \n",
            "2     3   84030          James King  2394.70198              170   \n",
            "3     4   82827              Giulio  2424.24309              104   \n",
            "4     5   80191           Domcastro  2427.05483              216   \n",
            "\n",
            "  TeamMemberUserNames  Year  Month  Day  \n",
            "0         davidthaler  2014      5    5  \n",
            "1            sriharij  2014      5    5  \n",
            "2           jfkingiii  2014      5    4  \n",
            "3           adjgiulio  2014      4   28  \n",
            "4           domcastro  2014      5    5  \n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Model Training and Evaluation"
      ],
      "metadata": {
        "id": "vfo7Kw_AYby8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Convert categorical variables to numeric using one-hot encoding\n",
        "X = df.drop(columns=['Score'])  # Adjust based on your target variable\n",
        "y = df['Score']  # Target variable\n",
        "\n",
        "# Identify categorical columns and apply one-hot encoding\n",
        "categorical_cols = X.select_dtypes(include=['object']).columns\n",
        "X = pd.get_dummies(X, columns=categorical_cols, drop_first=True)\n",
        "\n",
        "# Split the data into training and testing sets\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
        "\n",
        "# Train the Random Forest model\n",
        "model = RandomForestRegressor(n_estimators=100, random_state=42)\n",
        "model.fit(X_train, y_train)\n",
        "\n",
        "# Make predictions on the test set\n",
        "y_pred = model.predict(X_test)\n",
        "\n",
        "# Evaluate the model\n",
        "mse = mean_squared_error(y_test, y_pred)\n",
        "rmse = np.sqrt(mse)\n",
        "r2 = r2_score(y_test, y_pred)\n",
        "\n",
        "print(f\"Root Mean Squared Error: {rmse}\")\n",
        "print(f\"R-squared: {r2}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "c4HvZ5_FkAHt",
        "outputId": "7b9aa0e0-f515-4957-d681-d75c948f44f4"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Root Mean Squared Error: 21786.298600351474\n",
            "R-squared: -0.3413115964648241\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Feature Importance Analysis"
      ],
      "metadata": {
        "id": "Ly3qFl7BYied"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Feature Importance\n",
        "importance = model.feature_importances_\n",
        "feature_importance = pd.DataFrame({'Feature': X.columns, 'Importance': importance})\n",
        "feature_importance = feature_importance.sort_values(by='Importance', ascending=False)\n",
        "\n",
        "# Plot feature importance\n",
        "plt.figure(figsize=(10, 6))\n",
        "sns.barplot(x='Importance', y='Feature', data=feature_importance)\n",
        "plt.title('Feature Importance')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "rHN08lJ-kIys",
        "outputId": "775f781f-6a9b-4aa8-ba3a-3a4ea503c6cc"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
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            "/usr/local/lib/python3.10/dist-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 50725 (\\N{HANGUL SYLLABLE OG}) missing from current font.\n",
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            "/usr/local/lib/python3.10/dist-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 50976 (\\N{HANGUL SYLLABLE YU}) missing from current font.\n",
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            "/usr/local/lib/python3.10/dist-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 54984 (\\N{HANGUL SYLLABLE HUN}) missing from current font.\n",
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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Model Saving for Production Use"
      ],
      "metadata": {
        "id": "Cw80EeDlYnjz"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Save the Model\n",
        "joblib.dump(model, 'inventory_management_model.pkl')\n",
        "print(\"Model saved as 'inventory_management_model.pkl'.\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4LGUlpM0nc24",
        "outputId": "f0938dcb-fe7f-48d0-bac0-04a290cff451"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model saved as 'inventory_management_model.pkl'.\n"
          ]
        }
      ]
    }
  ]
}