2. Implementing and Visualisation LDA-checkpoint.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Linear Discriminant Analysis\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "data=pd.read_csv('Iris.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\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>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",
       "    </tr>\n",
       "    <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",
       "</table>\n",
       "</div>"
      ],
      "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": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "data=data.drop('Id',axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of X is  (150, 4)\n",
      "Shape of Y is  (150,)\n"
     ]
    }
   ],
   "source": [
    "#Splitting the dependent and independent variables\n",
    "X=data.iloc[:,:-1]\n",
    "Y=data['Species']\n",
    "print(\"Shape of X is \",X.shape)\n",
    "print(\"Shape of Y is \",Y.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "#LDA\n",
    "\n",
    "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis as LDA\n",
    "\n",
    "lda=LDA(n_components=2)\n",
    "x_lda= lda.fit_transform(X,Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "       [ 5.90148603,  2.32075134],\n",
       "       [ 4.68400868,  0.32508073]])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_lda"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>x_lda1</th>\n",
       "      <th>x_lda2</th>\n",
       "      <th>Species</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-8.084953</td>\n",
       "      <td>0.328454</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-7.147163</td>\n",
       "      <td>-0.755473</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-7.511378</td>\n",
       "      <td>-0.238078</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-6.837676</td>\n",
       "      <td>-0.642885</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-8.157814</td>\n",
       "      <td>0.540639</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "     x_lda1    x_lda2      Species\n",
       "0 -8.084953  0.328454  Iris-setosa\n",
       "1 -7.147163 -0.755473  Iris-setosa\n",
       "2 -7.511378 -0.238078  Iris-setosa\n",
       "3 -6.837676 -0.642885  Iris-setosa\n",
       "4 -8.157814  0.540639  Iris-setosa"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.DataFrame({'x_lda1': x_lda[:,0], 'x_lda2':x_lda[:,1],'Species':Y })\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x217e3748548>"
      ]
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     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot(df['x_lda1'],df['x_lda2'],hue=df['Species'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}