03. One Hot Encoding.ipynb

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    "## One Hot Encoding \n",
    "\n",
    "**There are 2 types of catogorical variables :- 1) Nominal ex.- Male,female; Pune ,Mumbai,Kolkata; green,red ,blue;  2)Ordinal ex.- high,medium,low; graduate ,masters ,phD;(they have some sort of numerical ordering in them)**\n",
    "\n",
    "Therefore in case of Nominal categorical variables we use in One Hot Encoding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
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   "source": [
    "import pandas as pd\n",
    "from sklearn import linear_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>town</th>\n",
       "      <th>area</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Pune</td>\n",
       "      <td>2600</td>\n",
       "      <td>550000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Pune</td>\n",
       "      <td>3000</td>\n",
       "      <td>565000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Pune</td>\n",
       "      <td>3200</td>\n",
       "      <td>610000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Pune</td>\n",
       "      <td>3600</td>\n",
       "      <td>680000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Pune</td>\n",
       "      <td>4000</td>\n",
       "      <td>725000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Mumbai</td>\n",
       "      <td>2600</td>\n",
       "      <td>585000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Mumbai</td>\n",
       "      <td>2800</td>\n",
       "      <td>615000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Mumbai</td>\n",
       "      <td>3300</td>\n",
       "      <td>650000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Mumbai</td>\n",
       "      <td>3600</td>\n",
       "      <td>710000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Banglore</td>\n",
       "      <td>2600</td>\n",
       "      <td>575000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Banglore</td>\n",
       "      <td>2900</td>\n",
       "      <td>600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Banglore</td>\n",
       "      <td>3100</td>\n",
       "      <td>620000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Banglore</td>\n",
       "      <td>3600</td>\n",
       "      <td>695000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      "text/plain": [
       "        town  area   price\n",
       "0       Pune  2600  550000\n",
       "1       Pune  3000  565000\n",
       "2       Pune  3200  610000\n",
       "3       Pune  3600  680000\n",
       "4       Pune  4000  725000\n",
       "5     Mumbai  2600  585000\n",
       "6     Mumbai  2800  615000\n",
       "7     Mumbai  3300  650000\n",
       "8     Mumbai  3600  710000\n",
       "9   Banglore  2600  575000\n",
       "10  Banglore  2900  600000\n",
       "11  Banglore  3100  620000\n",
       "12  Banglore  3600  695000"
      ]
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   "source": [
    "df=pd.read_csv(\"/Users/sudhanshukumar/Documents/Machine Learning/csv files/Sample_Home_Prices.csv\")\n",
    "df"
   ]
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       "    Banglore  Mumbai  Pune\n",
       "0          0       0     1\n",
       "1          0       0     1\n",
       "2          0       0     1\n",
       "3          0       0     1\n",
       "4          0       0     1\n",
       "5          0       1     0\n",
       "6          0       1     0\n",
       "7          0       1     0\n",
       "8          0       1     0\n",
       "9          1       0     0\n",
       "10         1       0     0\n",
       "11         1       0     0\n",
       "12         1       0     0"
      ]
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     "execution_count": 7,
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   "source": [
    "dummies =pd.get_dummies(df.town)   #creates dummy variables\n",
    "dummies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
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       "      <td>Mumbai</td>\n",
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       "      <td>0</td>\n",
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       "      <td>Banglore</td>\n",
       "      <td>2600</td>\n",
       "      <td>575000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Banglore</td>\n",
       "      <td>2900</td>\n",
       "      <td>600000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>Banglore</td>\n",
       "      <td>3100</td>\n",
       "      <td>620000</td>\n",
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       "      <td>0</td>\n",
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       "      <td>Banglore</td>\n",
       "      <td>3600</td>\n",
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      "text/plain": [
       "        town  area   price  Banglore  Mumbai  Pune\n",
       "0       Pune  2600  550000         0       0     1\n",
       "1       Pune  3000  565000         0       0     1\n",
       "2       Pune  3200  610000         0       0     1\n",
       "3       Pune  3600  680000         0       0     1\n",
       "4       Pune  4000  725000         0       0     1\n",
       "5     Mumbai  2600  585000         0       1     0\n",
       "6     Mumbai  2800  615000         0       1     0\n",
       "7     Mumbai  3300  650000         0       1     0\n",
       "8     Mumbai  3600  710000         0       1     0\n",
       "9   Banglore  2600  575000         1       0     0\n",
       "10  Banglore  2900  600000         1       0     0\n",
       "11  Banglore  3100  620000         1       0     0\n",
       "12  Banglore  3600  695000         1       0     0"
      ]
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     "execution_count": 18,
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   "source": [
    "merged=pd.concat([df,dummies],axis=\"columns\")  #merges dummy variables with the original\n",
    "merged        #now we don't need original \"town\" column so we'll drop it"
   ]
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    "### Dummy variable trap:\n",
    "**The Dummy variable trap is a scenario where there are attributes which are highly correlated (Multicollinear) and one variable predicts the value of others. Hence, one dummy variable is highly correlated with other dummy variables. Using all dummy variables for regression models lead to dummy variable trap.**"
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       "      <td>0</td>\n",
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      "text/plain": [
       "    area   price  Banglore  Mumbai\n",
       "0   2600  550000         0       0\n",
       "1   3000  565000         0       0\n",
       "2   3200  610000         0       0\n",
       "3   3600  680000         0       0\n",
       "4   4000  725000         0       0\n",
       "5   2600  585000         0       1\n",
       "6   2800  615000         0       1\n",
       "7   3300  650000         0       1\n",
       "8   3600  710000         0       1\n",
       "9   2600  575000         1       0\n",
       "10  2900  600000         1       0\n",
       "11  3100  620000         1       0\n",
       "12  3600  695000         1       0"
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   ],
   "source": [
    "## final=merged.drop([\"town\",\"Pune\"],axis=1) \n",
    "final  # its a good practice to drop manually"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "X=final.drop(\"price\",axis=1)  #axis=1 refers to columns and 0 for rows\n",
    "y=final.price"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "reg=linear_model.LinearRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg.fit(X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([590775.63964739])"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg.predict([[2800,1,0]])   # 1,0 means Bangalore"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([641227.69296925])"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg.predict([[3400,0,0]])   #0,0 means pune    #0,1 means Mumbai"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9573929037221872"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg.score(X,y)  #95% accurate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "# using inbuit encoder of sklearn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "le= LabelEncoder()    #creating object"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": true
   },
   "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>town</th>\n",
       "      <th>area</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>2600</td>\n",
       "      <td>550000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>3000</td>\n",
       "      <td>565000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>3200</td>\n",
       "      <td>610000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>3600</td>\n",
       "      <td>680000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>4000</td>\n",
       "      <td>725000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1</td>\n",
       "      <td>2600</td>\n",
       "      <td>585000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1</td>\n",
       "      <td>2800</td>\n",
       "      <td>615000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1</td>\n",
       "      <td>3300</td>\n",
       "      <td>650000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1</td>\n",
       "      <td>3600</td>\n",
       "      <td>710000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0</td>\n",
       "      <td>2600</td>\n",
       "      <td>575000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0</td>\n",
       "      <td>2900</td>\n",
       "      <td>600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0</td>\n",
       "      <td>3100</td>\n",
       "      <td>620000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0</td>\n",
       "      <td>3600</td>\n",
       "      <td>695000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    town  area   price\n",
       "0      2  2600  550000\n",
       "1      2  3000  565000\n",
       "2      2  3200  610000\n",
       "3      2  3600  680000\n",
       "4      2  4000  725000\n",
       "5      1  2600  585000\n",
       "6      1  2800  615000\n",
       "7      1  3300  650000\n",
       "8      1  3600  710000\n",
       "9      0  2600  575000\n",
       "10     0  2900  600000\n",
       "11     0  3100  620000\n",
       "12     0  3600  695000"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.town=le.fit_transform(df.town)   #converting string(names) of town into numeric\n",
    "df "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [],
   "source": [
    "X= df[[\"town\",\"area\"]].values\n",
    "y=df.price.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import OneHotEncoder\n",
    "ohe=OneHotEncoder()\n",
    "from sklearn.compose import ColumnTransformer\n",
    "ct=ColumnTransformer([('town',ohe,[0])],remainder=\"passthrough\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.0e+00, 0.0e+00, 1.0e+00, 2.6e+03],\n",
       "       [0.0e+00, 0.0e+00, 1.0e+00, 3.0e+03],\n",
       "       [0.0e+00, 0.0e+00, 1.0e+00, 3.2e+03],\n",
       "       [0.0e+00, 0.0e+00, 1.0e+00, 3.6e+03],\n",
       "       [0.0e+00, 0.0e+00, 1.0e+00, 4.0e+03],\n",
       "       [0.0e+00, 1.0e+00, 0.0e+00, 2.6e+03],\n",
       "       [0.0e+00, 1.0e+00, 0.0e+00, 2.8e+03],\n",
       "       [0.0e+00, 1.0e+00, 0.0e+00, 3.3e+03],\n",
       "       [0.0e+00, 1.0e+00, 0.0e+00, 3.6e+03],\n",
       "       [1.0e+00, 0.0e+00, 0.0e+00, 2.6e+03],\n",
       "       [1.0e+00, 0.0e+00, 0.0e+00, 2.9e+03],\n",
       "       [1.0e+00, 0.0e+00, 0.0e+00, 3.1e+03],\n",
       "       [1.0e+00, 0.0e+00, 0.0e+00, 3.6e+03]])"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X=ct.fit_transform(X)\n",
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg.fit(X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([565089.22812299])"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg.predict([[0,0,1,2800]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9573929037221874"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg.score(X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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