Sales_Prediction_Analysis_using_Random_forest_ML_Model.ipynb

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        "<a href=\"https://colab.research.google.com/github/ArfaKhalid/Machine-Learning-Models/blob/main/Sales_Prediction_Analysis_using_Random_forest_ML_Model.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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    {
      "cell_type": "markdown",
      "source": [
        "# Import Libraries"
      ],
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    {
      "cell_type": "code",
      "execution_count": 1,
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        "id": "Omg8skzINsxk"
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      "source": [
        "# import libraries\n",
        "import pandas as pd\n",
        "from sklearn.metrics import mean_absolute_error\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.tree import DecisionTreeRegressor\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Load Data"
      ],
      "metadata": {
        "id": "2ErD5yq3OQTd"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Path of the file to read\n",
        "iowa_file_path = 'train.csv'\n",
        "\n",
        "home_data = pd.read_csv(iowa_file_path)"
      ],
      "metadata": {
        "id": "hxGFuGN6OSMQ"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Feature Selection"
      ],
      "metadata": {
        "id": "R9NRqMS3OWQ9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Create target object and call it y\n",
        "y = home_data.SalePrice\n",
        "# Create X\n",
        "features = ['LotArea', 'YearBuilt', '1stFlrSF', '2ndFlrSF', 'FullBath', 'BedroomAbvGr', 'TotRmsAbvGrd']\n",
        "X = home_data[features]\n"
      ],
      "metadata": {
        "id": "BKhHsnIXOYQj"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Split Data"
      ],
      "metadata": {
        "id": "tOaMwFSbOf0j"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Split into validation and training data\n",
        "train_X, val_X, train_y, val_y = train_test_split(X, y, random_state=1)"
      ],
      "metadata": {
        "id": "sgW6T73-OhKB"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Model Building"
      ],
      "metadata": {
        "id": "ZOhxKkH_OkWL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Specify Model\n",
        "iowa_model = DecisionTreeRegressor(random_state=1)\n",
        "# Fit Model\n",
        "iowa_model.fit(train_X, train_y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
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        "id": "bE51UwuZOl5n",
        "outputId": "4ee21def-f63d-48af-9ac0-778b02d4533c"
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      "execution_count": 5,
      "outputs": [
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          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DecisionTreeRegressor(random_state=1)"
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            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Predictions"
      ],
      "metadata": {
        "id": "UTbBse6vOolg"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Make validation predictions and calculate mean absolute error\n",
        "val_predictions = iowa_model.predict(val_X)\n",
        "val_mae = mean_absolute_error(val_predictions, val_y)\n",
        "print(\"Validation MAE when not specifying max_leaf_nodes: {:,.0f}\".format(val_mae))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3nqoK3SMOrCp",
        "outputId": "f9a93f3b-895a-430b-9b40-b625dda3a646"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Validation MAE when not specifying max_leaf_nodes: 29,653\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Select Best Value"
      ],
      "metadata": {
        "id": "r_OG-yZOOvUF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Using best value for max_leaf_nodes\n",
        "iowa_model = DecisionTreeRegressor(max_leaf_nodes=100, random_state=1)\n",
        "iowa_model.fit(train_X, train_y)\n",
        "val_predictions = iowa_model.predict(val_X)\n",
        "val_mae = mean_absolute_error(val_predictions, val_y)\n",
        "print(\"Validation MAE for best value of max_leaf_nodes: {:,.0f}\".format(val_mae))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cdOFxZB5OxQZ",
        "outputId": "be2fb384-3a01-4abc-a246-8318904e3662"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Validation MAE for best value of max_leaf_nodes: 27,283\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Random Forest Model"
      ],
      "metadata": {
        "id": "a0rshaROPMqr"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.ensemble import RandomForestRegressor\n",
        "# define model and set random_state 1\n",
        "rf_model = RandomForestRegressor(random_state=1)\n",
        "\n",
        "# fit your model\n",
        "rf_model.fit(train_X, train_y)\n",
        "\n",
        "# Calculate the mean absolute error of your Random Forest model on the validation data\n",
        "rf_val_predictions = rf_model.predict(val_X)\n",
        "rf_val_mae = mean_absolute_error(rf_val_predictions, val_y)\n",
        "print(\"Validation MAE for Random Forest Model: {}\".format(rf_val_mae))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3-F0u-6WPPks",
        "outputId": "c94f0257-7e9f-4286-9836-e978bc94fcaa"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Validation MAE for Random Forest Model: 21857.15912981083\n"
          ]
        }
      ]
    }
  ]
}