iris-flower-classification-logistic-guided.ipynb

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     "text": [
      "/kaggle/input/iris/Iris.csv\n",
      "/kaggle/input/iris/database.sqlite\n"
     ]
    }
   ],
   "source": [
    "# Imports\n",
    "import numpy as np \n",
    "import pandas as pd \n",
    "import seaborn\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import accuracy_score\n",
    "%matplotlib inline\n",
    "\n",
    "\n",
    "# Input data files are available in the read-only \"../input/\" directory\n",
    "# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n",
    "\n",
    "import os\n",
    "for dirname, _, filenames in os.walk('/kaggle/input'):\n",
    "    for filename in filenames:\n",
    "        print(os.path.join(dirname, filename))"
   ]
  },
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>SepalLengthCm</th>\n",
       "      <th>SepalWidthCm</th>\n",
       "      <th>PetalLengthCm</th>\n",
       "      <th>PetalWidthCm</th>\n",
       "      <th>Species</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "   Id  SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm      Species\n",
       "0   1            5.1           3.5            1.4           0.2  Iris-setosa\n",
       "1   2            4.9           3.0            1.4           0.2  Iris-setosa\n",
       "2   3            4.7           3.2            1.3           0.2  Iris-setosa\n",
       "3   4            4.6           3.1            1.5           0.2  Iris-setosa\n",
       "4   5            5.0           3.6            1.4           0.2  Iris-setosa"
      ]
     },
     "execution_count": 2,
     "metadata": {},
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   ],
   "source": [
    "# Path\n",
    "path = \"/kaggle/input/iris/Iris.csv\"\n",
    "\n",
    "# Loading the dataset\n",
    "df =  pd.read_csv(path)\n",
    "\n",
    "# First few data\n",
    "df.head()"
   ]
  },
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Id</th>\n",
       "      <td>150.0</td>\n",
       "      <td>75.500000</td>\n",
       "      <td>43.445368</td>\n",
       "      <td>1.0</td>\n",
       "      <td>38.25</td>\n",
       "      <td>75.50</td>\n",
       "      <td>112.75</td>\n",
       "      <td>150.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SepalLengthCm</th>\n",
       "      <td>150.0</td>\n",
       "      <td>5.843333</td>\n",
       "      <td>0.828066</td>\n",
       "      <td>4.3</td>\n",
       "      <td>5.10</td>\n",
       "      <td>5.80</td>\n",
       "      <td>6.40</td>\n",
       "      <td>7.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SepalWidthCm</th>\n",
       "      <td>150.0</td>\n",
       "      <td>3.054000</td>\n",
       "      <td>0.433594</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.80</td>\n",
       "      <td>3.00</td>\n",
       "      <td>3.30</td>\n",
       "      <td>4.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PetalLengthCm</th>\n",
       "      <td>150.0</td>\n",
       "      <td>3.758667</td>\n",
       "      <td>1.764420</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.60</td>\n",
       "      <td>4.35</td>\n",
       "      <td>5.10</td>\n",
       "      <td>6.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PetalWidthCm</th>\n",
       "      <td>150.0</td>\n",
       "      <td>1.198667</td>\n",
       "      <td>0.763161</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.30</td>\n",
       "      <td>1.30</td>\n",
       "      <td>1.80</td>\n",
       "      <td>2.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "               count       mean        std  min    25%    50%     75%    max\n",
       "Id             150.0  75.500000  43.445368  1.0  38.25  75.50  112.75  150.0\n",
       "SepalLengthCm  150.0   5.843333   0.828066  4.3   5.10   5.80    6.40    7.9\n",
       "SepalWidthCm   150.0   3.054000   0.433594  2.0   2.80   3.00    3.30    4.4\n",
       "PetalLengthCm  150.0   3.758667   1.764420  1.0   1.60   4.35    5.10    6.9\n",
       "PetalWidthCm   150.0   1.198667   0.763161  0.1   0.30   1.30    1.80    2.5"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "# Basic statistic\n",
    "df.describe().T"
   ]
  },
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Species</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      "text/plain": [
       "            Species\n",
       "count           150\n",
       "unique            3\n",
       "top     Iris-setosa\n",
       "freq             50"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Basic Statistic - Object\n",
    "df.describe(include = \"object\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Iris-setosa', 'Iris-versicolor', 'Iris-virginica'], dtype=object)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "# Unique values in species column\n",
    "df[\"Species\"].unique()"
   ]
  },
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     "data": {
      "text/plain": [
       "array([[<Axes: title={'center': 'Id'}>,\n",
       "        <Axes: title={'center': 'SepalLengthCm'}>],\n",
       "       [<Axes: title={'center': 'SepalWidthCm'}>,\n",
       "        <Axes: title={'center': 'PetalLengthCm'}>],\n",
       "       [<Axes: title={'center': 'PetalWidthCm'}>, <Axes: >]], dtype=object)"
      ]
     },
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",
      "text/plain": [
       "<Figure size 1500x1500 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Distribution plot\n",
    "df.hist(figsize=(15,15))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "4e842b0e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-02T12:35:04.070930Z",
     "iopub.status.busy": "2024-01-02T12:35:04.070590Z",
     "iopub.status.idle": "2024-01-02T12:35:04.078554Z",
     "shell.execute_reply": "2024-01-02T12:35:04.077591Z"
    },
    "papermill": {
     "duration": 0.018927,
     "end_time": "2024-01-02T12:35:04.080870",
     "exception": false,
     "start_time": "2024-01-02T12:35:04.061943",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Id               0\n",
       "SepalLengthCm    0\n",
       "SepalWidthCm     0\n",
       "PetalLengthCm    0\n",
       "PetalWidthCm     0\n",
       "Species          0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking for missing values\n",
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "c22a65b4",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-02T12:35:04.093992Z",
     "iopub.status.busy": "2024-01-02T12:35:04.093243Z",
     "iopub.status.idle": "2024-01-02T12:35:04.104923Z",
     "shell.execute_reply": "2024-01-02T12:35:04.103681Z"
    },
    "papermill": {
     "duration": 0.021292,
     "end_time": "2024-01-02T12:35:04.107554",
     "exception": false,
     "start_time": "2024-01-02T12:35:04.086262",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Dropping the id column\n",
    "df = df.drop(columns = \"Id\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "a046e9ce",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-02T12:35:04.124940Z",
     "iopub.status.busy": "2024-01-02T12:35:04.124022Z",
     "iopub.status.idle": "2024-01-02T12:35:04.139109Z",
     "shell.execute_reply": "2024-01-02T12:35:04.138126Z"
    },
    "papermill": {
     "duration": 0.026503,
     "end_time": "2024-01-02T12:35:04.141442",
     "exception": false,
     "start_time": "2024-01-02T12:35:04.114939",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SepalLengthCm</th>\n",
       "      <th>SepalWidthCm</th>\n",
       "      <th>PetalLengthCm</th>\n",
       "      <th>PetalWidthCm</th>\n",
       "      <th>Species</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm      Species\n",
       "0            5.1           3.5            1.4           0.2  Iris-setosa\n",
       "1            4.9           3.0            1.4           0.2  Iris-setosa\n",
       "2            4.7           3.2            1.3           0.2  Iris-setosa\n",
       "3            4.6           3.1            1.5           0.2  Iris-setosa\n",
       "4            5.0           3.6            1.4           0.2  Iris-setosa"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "88a46f20",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-02T12:35:04.156423Z",
     "iopub.status.busy": "2024-01-02T12:35:04.155763Z",
     "iopub.status.idle": "2024-01-02T12:35:04.161289Z",
     "shell.execute_reply": "2024-01-02T12:35:04.160138Z"
    },
    "papermill": {
     "duration": 0.01511,
     "end_time": "2024-01-02T12:35:04.163678",
     "exception": false,
     "start_time": "2024-01-02T12:35:04.148568",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Seperating features and label\n",
    "features = df.drop(columns=\"Species\")\n",
    "label = df['Species']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "46bc5880",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-02T12:35:04.176994Z",
     "iopub.status.busy": "2024-01-02T12:35:04.176279Z",
     "iopub.status.idle": "2024-01-02T12:35:04.496889Z",
     "shell.execute_reply": "2024-01-02T12:35:04.495620Z"
    },
    "papermill": {
     "duration": 0.329414,
     "end_time": "2024-01-02T12:35:04.498885",
     "exception": false,
     "start_time": "2024-01-02T12:35:04.169471",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Correlation Heatmap\n",
    "seaborn.heatmap(features.corr(), annot=True, fmt= \".0%\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "a82b7e28",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-02T12:35:04.512733Z",
     "iopub.status.busy": "2024-01-02T12:35:04.511843Z",
     "iopub.status.idle": "2024-01-02T12:35:04.518193Z",
     "shell.execute_reply": "2024-01-02T12:35:04.517297Z"
    },
    "papermill": {
     "duration": 0.015464,
     "end_time": "2024-01-02T12:35:04.520342",
     "exception": false,
     "start_time": "2024-01-02T12:35:04.504878",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Splitting the dataset\n",
    "ftrain, ftest, ltrain, ltest = train_test_split(features, label, test_size = 0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "e62ce5e2",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-02T12:35:04.534196Z",
     "iopub.status.busy": "2024-01-02T12:35:04.533863Z",
     "iopub.status.idle": "2024-01-02T12:35:04.569684Z",
     "shell.execute_reply": "2024-01-02T12:35:04.568585Z"
    },
    "papermill": {
     "duration": 0.045169,
     "end_time": "2024-01-02T12:35:04.571753",
     "exception": false,
     "start_time": "2024-01-02T12:35:04.526584",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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      ],
      "text/plain": [
       "LogisticRegression()"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Training the model\n",
    "model = LogisticRegression()\n",
    "model.fit(ftrain, ltrain)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "4220e2e3",
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    "tags": []
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   "outputs": [],
   "source": [
    "# Prediction\n",
    "prediction = model.predict(ftest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "92a01df8",
   "metadata": {
    "execution": {
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     "status": "completed"
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Evaluation score\n",
    "accuracy_score(prediction, ltest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "778bbb6f",
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   "source": []
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