SVM_SKlearn.ipynb

{
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  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyOGJZyA2yc1vqqcMtak9JPt"
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "wQWCJv1SYI2U"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "from sklearn.svm import LinearSVC"
      ]
    },
    {
      "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": "mEYan8zoYtSl"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model = LinearSVC()\n",
        "model.fit (X,Y)\n",
        "predictions = model.predict([ [21,1], [22, 0] ])\n",
        "predictions"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ItagWsmnY2Cb",
        "outputId": "9c33ca32-5231-42ba-b502-aca372c15b45"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.8/dist-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but LinearSVC was fitted with feature names\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['HipHop', 'Classical'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import accuracy_score"
      ],
      "metadata": {
        "id": "quUNgSQiZRsK"
      },
      "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\n",
        "\n",
        "\n",
        "print(predictions)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RVRgg7HNZYBE",
        "outputId": "e4d1782d-b80f-4863-e080-9a9a19c382e9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['Classical' 'Classical' 'Classical' 'Classical']\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.8/dist-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
            "  warnings.warn(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "vXqwfgHPZhsy"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}