preprocessing.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.decomposition import PCA\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('./data/iris.data', names=['sepal_length','sepal_width','petal_length','petal_width','target'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "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>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "      <th>target</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": [
       "   sepal_length  sepal_width  petal_length  petal_width       target\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": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 1) Looking for NaN data in each column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def count_Nans():\n",
    "    nans = {}\n",
    "    for i in df.columns:\n",
    "        nans[i] = df[i].isna().sum()\n",
    "    return nans"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of NaNs for each attribute:\n",
      "{'sepal_length': 2, 'sepal_width': 0, 'petal_length': 2, 'petal_width': 3, 'target': 3}\n"
     ]
    }
   ],
   "source": [
    "print('Number of NaNs for each attribute:')\n",
    "print(count_Nans())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 2) Delete lost data in the dataset using `dropna`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of NaNs after handling of missing data:\n",
      "{'sepal_length': 0, 'sepal_width': 0, 'petal_length': 0, 'petal_width': 0, 'target': 0}\n"
     ]
    }
   ],
   "source": [
    "df = df.dropna()\n",
    "print('Number of NaNs after handling of missing data:')\n",
    "print(count_Nans())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 3) Convert Target column to numerical label with `LabelEncoder`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   sepal_length  sepal_width  petal_length  petal_width  target\n",
      "0           5.1          3.5           1.4          0.2       0\n",
      "1           4.9          3.0           1.4          0.2       0\n",
      "2           4.7          3.2           1.3          0.2       0\n",
      "3           4.6          3.1           1.5          0.2       0\n",
      "4           5.0          3.6           1.4          0.2       0\n"
     ]
    }
   ],
   "source": [
    "labelEncoder = LabelEncoder()\n",
    "df['target'] = labelEncoder.fit_transform(df.target.values)\n",
    "print(df.head())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 4) Normalize data with `StandardScaler`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
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      ],
      "text/plain": [
       "StandardScaler()"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ss = StandardScaler()\n",
    "ss.fit(df.iloc[:, 0:4])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sepal_length: mean = 5.843333333333334 \t var = 0.6811222222222223\n",
      "sepal_width: mean = 3.0540000000000003 \t var = 0.18675066666666668\n",
      "petal_length: mean = 3.758666666666666 \t var = 3.092424888888889\n",
      "petal_width: mean = 1.1986666666666668 \t var = 0.5785315555555555\n"
     ]
    }
   ],
   "source": [
    "for col in range(len(df.columns) - 1):\n",
    "    print('{}: mean = {} \\t var = {}'.format(df.columns[col], ss.mean_[col], ss.var_[col]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.iloc[:, :-1] = ss.transform(df.iloc[:, :-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sepal_length: mean = -4.736951571734001e-16 \t var = 1.0067114093959733\n",
      "sepal_width: mean = -6.631732200427602e-16 \t var = 1.006711409395973\n",
      "petal_length: mean = 3.315866100213801e-16 \t var = 1.0067114093959728\n",
      "petal_width: mean = -2.842170943040401e-16 \t var = 1.0067114093959733\n"
     ]
    }
   ],
   "source": [
    "for col in range(len(df.columns) - 1):\n",
    "    print('{}: mean = {} \\t var = {}'.format(df.columns[col], df.iloc[:, col].mean(), df.iloc[:, col].var()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 5) Dimension Reduction with `PCA`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "pca = PCA(n_components=2)\n",
    "pca.fit(df.iloc[:, :-1])\n",
    "reduced = pca.transform(df.iloc[:, :-1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 6) Scatter plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "figure = plt.figure()\n",
    "axes = figure.add_subplot()\n",
    "scatter = axes.scatter(x=reduced[:, 0], y=reduced[:, 1], c=df.target, s=20, marker='x', cmap='jet', alpha=0.9)\n",
    "axes.legend(*scatter.legend_elements(), loc=\"best\", title=\"target\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 7) Box plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.boxplot(df.iloc[:, :-1], labels=df.columns[0:-1])\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.10.5 64-bit",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.5"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "f56a879a86e4cb184598e0f1a037d80ac9bf804487d5cf89fe943b503a6b43f5"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}