Innomatics Research lab Interview.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 593,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.svm import SVC\n",
    "from scipy.stats import randint\n",
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.linear_model import LogisticRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 309,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>x1</th>\n",
       "      <th>x2</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-119.366669</td>\n",
       "      <td>115.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-101.108044</td>\n",
       "      <td>97.777159</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-130.278658</td>\n",
       "      <td>106.767654</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-114.703415</td>\n",
       "      <td>101.195477</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-119.366669</td>\n",
       "      <td>115.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           x1          x2  y\n",
       "0 -119.366669  115.000000  1\n",
       "1 -101.108044   97.777159  1\n",
       "2 -130.278658  106.767654  1\n",
       "3 -114.703415  101.195477  1\n",
       "4 -119.366669  115.000000  1"
      ]
     },
     "execution_count": 309,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv('/home/arsath/Downloads/data.csv')\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 310,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>x1</th>\n",
       "      <th>x2</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2222.000000</td>\n",
       "      <td>2.224000e+03</td>\n",
       "      <td>2227.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>-4.818766</td>\n",
       "      <td>8.947626e+04</td>\n",
       "      <td>0.442299</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>74.939909</td>\n",
       "      <td>2.983323e+06</td>\n",
       "      <td>0.496771</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-134.369160</td>\n",
       "      <td>-1.348761e+02</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-71.757030</td>\n",
       "      <td>-8.012983e+01</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>-9.836555</td>\n",
       "      <td>-1.046151e+00</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>59.813962</td>\n",
       "      <td>6.389086e+01</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>134.508687</td>\n",
       "      <td>1.000000e+08</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                x1            x2            y\n",
       "count  2222.000000  2.224000e+03  2227.000000\n",
       "mean     -4.818766  8.947626e+04     0.442299\n",
       "std      74.939909  2.983323e+06     0.496771\n",
       "min    -134.369160 -1.348761e+02     0.000000\n",
       "25%     -71.757030 -8.012983e+01     0.000000\n",
       "50%      -9.836555 -1.046151e+00     0.000000\n",
       "75%      59.813962  6.389086e+01     1.000000\n",
       "max     134.508687  1.000000e+08     1.000000"
      ]
     },
     "execution_count": 310,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 311,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>x1</th>\n",
       "      <th>x2</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-134.369160</td>\n",
       "      <td>-1.348761e+02</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>134.508687</td>\n",
       "      <td>1.000000e+08</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             x1            x2  y\n",
       "min -134.369160 -1.348761e+02  0\n",
       "max  134.508687  1.000000e+08  1"
      ]
     },
     "execution_count": 311,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.agg((min,max))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Since there are only very few missing values, dropping them is not going to affect data at all\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 312,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2219, 3)"
      ]
     },
     "execution_count": 312,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.dropna(axis = 0,inplace = True)\n",
    "data.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Another way of handling missing value is to impute it by training a model and predicting the values based on other features.\n",
    "\n",
    "### One such way is using KNN Imputer from sklearn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 313,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1541    9.901000e+07\n",
       "1948    1.349297e+02\n",
       "2226    1.000000e+08\n",
       "Name: x2, dtype: float64"
      ]
     },
     "execution_count": 313,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "thresh_hold = np.percentile(data['x2'],99.9)\n",
    "to_drop = data['x2'][data['x2'] > thresh_hold ]\n",
    "to_drop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 314,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.drop(to_drop.index,inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 315,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 315,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data['x2'].plot(kind = 'box')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 316,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='x1', ylabel='Count'>"
      ]
     },
     "execution_count": 316,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot(data['x1'],kde = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 317,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "x1    False\n",
       "x2    False\n",
       "y     False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 317,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isna().any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 318,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 318,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.plot(kind = 'box')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 319,
   "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>x1</th>\n",
       "      <th>x2</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>x1</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.108637</td>\n",
       "      <td>-0.119497</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>x2</th>\n",
       "      <td>0.108637</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.063953</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>y</th>\n",
       "      <td>-0.119497</td>\n",
       "      <td>0.063953</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          x1        x2         y\n",
       "x1  1.000000  0.108637 -0.119497\n",
       "x2  0.108637  1.000000  0.063953\n",
       "y  -0.119497  0.063953  1.000000"
      ]
     },
     "execution_count": 319,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 320,
   "metadata": {},
   "outputs": [],
   "source": [
    "st = StandardScaler().fit_transform(data[['x1','x2']])\n",
    "new_data = pd.DataFrame(st,index = data.index,columns=['x1','x2'])\n",
    "\n",
    "X = new_data\n",
    "y = data.loc[:,'y']\n",
    "X_train,X_test,y_train,y_test = train_test_split(X,y,test_size = 0.30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 340,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 340,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "new_data.plot(kind = 'box',grid = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 405,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9338345864661655"
      ]
     },
     "execution_count": 405,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "decclass = DecisionTreeClassifier()\n",
    "decclass.fit(X_train,y_train)\n",
    "pred_dec_tree = decclass.predict(X_test)\n",
    "accuracy_score(y_test,pred_dec_tree)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 566,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9503532992354617"
      ]
     },
     "execution_count": 566,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "\n",
    "param_dist_dec_tree = {\"max_depth\": [3,None],\n",
    "              \"max_features\": randint(1, 3),\n",
    "              \"min_samples_leaf\": randint(1, 9),\n",
    "              \"criterion\": [\"gini\", \"entropy\"]\n",
    "        }\n",
    "\n",
    "rscv_dec = RandomizedSearchCV(decclass,param_distributions=param_dist_dec_tree,cv = 4,n_iter = 50,n_jobs = -1)\n",
    "rscv_dec.fit(X_train,y_train)\n",
    "rscv_dec.best_score_\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 335,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9473684210526315"
      ]
     },
     "execution_count": 335,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svc = SVC()\n",
    "svc.fit(X_train,y_train)\n",
    "pred_svc = svc.predict(X_test)\n",
    "accuracy_score(y_test,pred_svc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 336,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7413533834586467"
      ]
     },
     "execution_count": 336,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svc_lin = SVC(kernel='linear')\n",
    "svc_lin.fit(X_train,y_train)\n",
    "predict_svc_lin = svc_lin.predict(X_test)\n",
    "accuracy_score(y_test,predict_svc_lin)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 567,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9535716188497296"
      ]
     },
     "execution_count": 567,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "param_dist_svc  = {\n",
    "    'C' : [1.0,1.5,2.0,2.5],\n",
    "    'kernel' : ['linear','rbf','poly','sigmoid'],\n",
    "    'degree' : [3,4,5,6],    \n",
    "}\n",
    "rscv_svc = RandomizedSearchCV(svc,param_distributions=param_dist_svc,cv = 4,n_iter = 50,n_jobs = -1)\n",
    "rscv_svc.fit(X_train,y_train)\n",
    "rscv_svc.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 337,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9323308270676691"
      ]
     },
     "execution_count": 337,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "knn = KNeighborsClassifier(n_jobs = -1)\n",
    "knn.fit(X_train,y_train)\n",
    "pred_knn = knn.predict(X_test)\n",
    "accuracy_score(y_test,pred_knn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 568,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9535938792390404"
      ]
     },
     "execution_count": 568,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "param_dist_knn = {\n",
    "    'n_neighbors' : [5,7,9,11],\n",
    "    'weights' : ['uniform','distance'],\n",
    "}\n",
    "\n",
    "rscv_knn = RandomizedSearchCV(knn,param_distributions=param_dist_knn,n_jobs = -1,n_iter = 8,cv = 10)\n",
    "rscv_knn.fit(X_train,y_train)\n",
    "rscv_knn.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 338,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7488721804511278"
      ]
     },
     "execution_count": 338,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "log = LogisticRegression()\n",
    "log.fit(X_train,y_train)\n",
    "pred_log = log.predict(X_test)\n",
    "accuracy_score(y_test,pred_log)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 587,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomizedSearchCV(cv=10, estimator=LogisticRegression(), n_jobs=-1,\n",
       "                   param_distributions={'fit_intercept': [True, False],\n",
       "                                        'max_iter': [50, 100, 130, 150],\n",
       "                                        'solver': ['newton-cg', 'lbfgs',\n",
       "                                                   'liblinear', 'sag', 'saga'],\n",
       "                                        'warm_start': [True, False]})"
      ]
     },
     "execution_count": 587,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "param_dist_log = {\n",
    "    'fit_intercept' : [True,False],\n",
    "    'solver' : ['newton-cg', 'lbfgs', 'liblinear', 'sag', 'saga'],\n",
    "    'warm_start' : [True,False],\n",
    "    'max_iter' : [50,100,130,150]\n",
    "}\n",
    "\n",
    "\n",
    "rscv_log = RandomizedSearchCV(log,param_distributions=param_dist_log,n_iter = 10,n_jobs = -1,cv = 10)\n",
    "rscv_log.fit(X_train,y_train)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 583,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9503532992354617"
      ]
     },
     "execution_count": 583,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dec_best = rscv_dec.best_score_\n",
    "dec_best"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 584,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9535938792390404"
      ]
     },
     "execution_count": 584,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn_best = rscv_knn.best_score_            \n",
    "knn_best"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 585,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9535716188497296"
      ]
     },
     "execution_count": 585,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svc_best = rscv_svc.best_score_\n",
    "svc_best"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 589,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7408395368072788"
      ]
     },
     "execution_count": 589,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "logistic_best = rscv_log.best_score_\n",
    "logistic_best"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 591,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 591,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x = ['DEC_best','KNN_best','SVC_best','Logistic_best'],y = [dec_best,knn_best,svc_best,logistic_best])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### From the Above plot it is clear that Decision tree, Support Vector Classifier and KNN performs very similarly and Logistic Regression performs worst in this case."
   ]
  },
  {
   "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": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}