Day_23_Machine_Learning_Classification_Project1.ipynb

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      "name": "Day_23_Machine_Learning_Classification_Project1.ipynb",
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      "cell_type": "markdown",
      "source": [
        "Classification algorithms are used for predictive modeling problem where input training data is used to predict the probability that future data will fall into one of the predetermined/labelled categories.\n",
        "\n",
        "<div style=\"text-align:center\"><img alt=\"Classification\" src=\"https://github.com/thunderstroke325/60-Days-of-Data-Science-and-ML/blob/main/assets/classification.jpg?raw=true\"/></div>\n",
        "\n",
        "Some of the most popular classification algorithms are\n",
        "*   Logistic Regression\n",
        "*   Naive Bayes\n",
        "*   K-Nearets Neihbors\n",
        "*   Decision Tree\n",
        "*   Support Vector Machines"
      ],
      "metadata": {
        "id": "GewlE61cmyVb"
      }
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    {
      "cell_type": "code",
      "source": [
        "!pip install squarify"
      ],
      "metadata": {
        "colab": {
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        },
        "id": "KhocjpG3m5Hk",
        "outputId": "f68c462e-f3b0-471b-d823-742f7f1573f0"
      },
      "execution_count": 14,
      "outputs": [
        {
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          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: squarify in /usr/local/lib/python3.7/dist-packages (0.4.3)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Import necessary libraries\n",
        "import numpy as np # linear algebra\n",
        "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
        "import seaborn as sns\n",
        "from matplotlib import pyplot as plt\n",
        "from matplotlib.colors import rgb2hex\n",
        "import matplotlib.cm as cm\n",
        "import plotly.express as px\n",
        "import plotly.graph_objects as go\n",
        "import squarify\n",
        "from plotly.offline import init_notebook_mode,iplot\n",
        "from wordcloud import WordCloud\n",
        "import pandas_profiling as pp\n",
        "from PIL import Image\n",
        "from sklearn.preprocessing import MultiLabelBinarizer\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "import matplotlib.colors\n",
        "from collections import Counter\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.ensemble import RandomForestRegressor, RandomForestClassifier\n",
        "from sklearn import metrics\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.svm import SVC\n",
        "from sklearn.metrics import confusion_matrix, classification_report, accuracy_score\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "from sklearn.metrics import  f1_score\n",
        "cmap2 = cm.get_cmap('twilight',13)\n",
        "colors1= []\n",
        "for i in range(cmap2.N):\n",
        "    rgb= cmap2(i)[:4]\n",
        "    colors1.append(rgb2hex(rgb))\n",
        "    #print(rgb2hex(rgb))\n",
        "# Set style\n",
        "sns.set(style='whitegrid')"
      ],
      "metadata": {
        "id": "ee7zEX9emvxi"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Load the data"
      ],
      "metadata": {
        "id": "eIQiGfdgmveL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df= pd.read_csv('https://github.com/raw/thunderstroke325/60-Days-of-Data-Science-and-ML/main/datasets/data.csv', low_memory=False)\n",
        "df"
      ],
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              "     class cap-shape cap-surface cap-color bruises odor gill-attachment  \\\n",
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              "     gill-spacing gill-size gill-color  ... stalk-surface-below-ring  \\\n",
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              "     stalk-color-above-ring stalk-color-below-ring veil-type veil-color  \\\n",
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              "          document.querySelector('#df-285df861-1a80-4959-b53d-e535a629df80 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-285df861-1a80-4959-b53d-e535a629df80');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BoyZAG-roEVk",
        "outputId": "cfe21e79-9a61-4ec2-d537-bb4057d71368"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 8124 entries, 0 to 8123\n",
            "Data columns (total 23 columns):\n",
            " #   Column                    Non-Null Count  Dtype \n",
            "---  ------                    --------------  ----- \n",
            " 0   class                     8124 non-null   object\n",
            " 1   cap-shape                 8124 non-null   object\n",
            " 2   cap-surface               8124 non-null   object\n",
            " 3   cap-color                 8124 non-null   object\n",
            " 4   bruises                   8124 non-null   object\n",
            " 5   odor                      8124 non-null   object\n",
            " 6   gill-attachment           8124 non-null   object\n",
            " 7   gill-spacing              8124 non-null   object\n",
            " 8   gill-size                 8124 non-null   object\n",
            " 9   gill-color                8124 non-null   object\n",
            " 10  stalk-shape               8124 non-null   object\n",
            " 11  stalk-root                8124 non-null   object\n",
            " 12  stalk-surface-above-ring  8124 non-null   object\n",
            " 13  stalk-surface-below-ring  8124 non-null   object\n",
            " 14  stalk-color-above-ring    8124 non-null   object\n",
            " 15  stalk-color-below-ring    8124 non-null   object\n",
            " 16  veil-type                 8124 non-null   object\n",
            " 17  veil-color                8124 non-null   object\n",
            " 18  ring-number               8124 non-null   object\n",
            " 19  ring-type                 8124 non-null   object\n",
            " 20  spore-print-color         8124 non-null   object\n",
            " 21  population                8124 non-null   object\n",
            " 22  habitat                   8124 non-null   object\n",
            "dtypes: object(23)\n",
            "memory usage: 1.4+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# shape of data\n",
        "df.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xP0EACKToIVn",
        "outputId": "d53f6687-c358-460f-c86c-86c9c61f8944"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(8124, 23)"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# missing values\n",
        "df.isna().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nx82Lf-4oMna",
        "outputId": "7ffe50aa-9105-4b88-e547-3fbb03636d5d"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "class                       0\n",
              "cap-shape                   0\n",
              "cap-surface                 0\n",
              "cap-color                   0\n",
              "bruises                     0\n",
              "odor                        0\n",
              "gill-attachment             0\n",
              "gill-spacing                0\n",
              "gill-size                   0\n",
              "gill-color                  0\n",
              "stalk-shape                 0\n",
              "stalk-root                  0\n",
              "stalk-surface-above-ring    0\n",
              "stalk-surface-below-ring    0\n",
              "stalk-color-above-ring      0\n",
              "stalk-color-below-ring      0\n",
              "veil-type                   0\n",
              "veil-color                  0\n",
              "ring-number                 0\n",
              "ring-type                   0\n",
              "spore-print-color           0\n",
              "population                  0\n",
              "habitat                     0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['class'].unique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4O8FjvugsFSl",
        "outputId": "21452f05-c86a-4b8a-c942-a5531d9cdfae"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['p', 'e'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['class'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NJPf8QSvsJHf",
        "outputId": "7df1101a-3642-4f23-d9fa-86b694060f9d"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "e    4208\n",
              "p    3916\n",
              "Name: class, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Data Visualization"
      ],
      "metadata": {
        "id": "OzXn_thimyMr"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "m_class = ('Poisonous', 'Edible')\n",
        "fig, ax = plt.subplots(figsize=(10, 5))\n",
        "sns.countplot(x='class', palette='mako', saturation=0.6, data=df,edgecolor='black',linewidth=0.2, dodge=True,ax=ax)\n",
        "ax.set_xticklabels(m_class)\n",
        "plt.xlabel('Mushroom Class')\n",
        "plt.ylabel('Quantity')\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "1aq4N4k-uQn0"
      },
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "m_shape = {\"bell\":\"b\",\"conical\":\"c\",\"convex\":\"x\",\"flat\":\"f\", \"knobbed\":\"k\",\"sunken\":\"s\"}\n",
        "labels = ('convex', 'bell', 'sunken', 'flat', 'knobbed', 'conical')\n",
        "fig, ax = plt.subplots(figsize=(10, 5))\n",
        "sns.countplot(x='cap-shape', palette='mako',hue='class', saturation=0.6, data=df,edgecolor='black',linewidth=0.2, dodge=True,ax=ax)\n",
        "ax.set_xticklabels(labels)\n",
        "plt.xlabel('Mushroom Cap Shape')\n",
        "plt.ylabel('Quantity')\n",
        "plt.grid(False)\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "_HSnsE52uY1j"
      },
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "oNCLz58TmyHT"
      }
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "C0Wj8hh_myE7"
      }
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "EMR24qgEmyCd"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "id": "_J3VAuQkl71k"
      },
      "outputs": [],
      "source": []
    }
  ]
}