Random_Forest_model_test_sklearn.ipynb

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      "name": "python"
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  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "TT9iVQyjUvdS"
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      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "from sklearn.ensemble import RandomForestClassifier"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "music_data = pd.read_csv('music.csv')\n",
        "X = music_data.drop (columns=['genre'])\n",
        "Y = music_data['genre']"
      ],
      "metadata": {
        "id": "ntJGw2uDU5pt"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model = RandomForestClassifier()\n",
        "model.fit (X,Y)\n",
        "predictions = model.predict([ [21,1], [22, 0] ])\n",
        "predictions"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "padsO8aFVzgM",
        "outputId": "f306a4e3-7e6d-4831-aca3-792a645ee7bf"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but RandomForestClassifier was fitted with feature names\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['HipHop', 'Dance'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import accuracy_score"
      ],
      "metadata": {
        "id": "MdewpO21V-F4"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# test_size=0.2\n",
        "#0.2 means 20%\n",
        "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.2)\n",
        "#here, X train test is input, Y train test is output.\n",
        "\n",
        "\n",
        "model.fit (X_train,Y_train)\n",
        "predictions = model.predict(X_test)\n",
        "\n",
        "score = accuracy_score(Y_test, predictions)\n",
        "score"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5aCPpWUeWH0C",
        "outputId": "f4ce547b-c8d5-4736-ad34-be638efed80a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.5"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
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    {
      "cell_type": "code",
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      "metadata": {
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