MNIST Dataset.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyforest import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('import pandas as pd'); }\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train = pd.read_csv(\"C:/Users/mukul/OneDrive/Documents/mnisttrain.csv\")\n",
    "test = pd.read_csv(\"C:/Users/mukul/OneDrive/Documents/mnisttest.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('import matplotlib.pyplot as plt\\nimport pandas as pd'); }\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "submission = pd.read_csv(\"C:/Users/mukul/OneDrive/Documents/mnistsample_submission.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(42000, 785)\n",
      "(28000, 784)\n"
     ]
    }
   ],
   "source": [
    "print(train.shape)\n",
    "print(test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 785 columns</p>\n",
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      ],
      "text/plain": [
       "   label  pixel0  pixel1  pixel2  pixel3  pixel4  pixel5  pixel6  pixel7  \\\n",
       "0      1       0       0       0       0       0       0       0       0   \n",
       "1      0       0       0       0       0       0       0       0       0   \n",
       "2      1       0       0       0       0       0       0       0       0   \n",
       "3      4       0       0       0       0       0       0       0       0   \n",
       "4      0       0       0       0       0       0       0       0       0   \n",
       "\n",
       "   pixel8  ...  pixel774  pixel775  pixel776  pixel777  pixel778  pixel779  \\\n",
       "0       0  ...         0         0         0         0         0         0   \n",
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       "3       0  ...         0         0         0         0         0         0   \n",
       "4       0  ...         0         0         0         0         0         0   \n",
       "\n",
       "   pixel780  pixel781  pixel782  pixel783  \n",
       "0         0         0         0         0  \n",
       "1         0         0         0         0  \n",
       "2         0         0         0         0  \n",
       "3         0         0         0         0  \n",
       "4         0         0         0         0  \n",
       "\n",
       "[5 rows x 785 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head() # Label is the Target Variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 784 columns</p>\n",
       "</div>"
      ],
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       "   pixel0  pixel1  pixel2  pixel3  pixel4  pixel5  pixel6  pixel7  pixel8  \\\n",
       "0       0       0       0       0       0       0       0       0       0   \n",
       "1       0       0       0       0       0       0       0       0       0   \n",
       "2       0       0       0       0       0       0       0       0       0   \n",
       "3       0       0       0       0       0       0       0       0       0   \n",
       "4       0       0       0       0       0       0       0       0       0   \n",
       "\n",
       "   pixel9  ...  pixel774  pixel775  pixel776  pixel777  pixel778  pixel779  \\\n",
       "0       0  ...         0         0         0         0         0         0   \n",
       "1       0  ...         0         0         0         0         0         0   \n",
       "2       0  ...         0         0         0         0         0         0   \n",
       "3       0  ...         0         0         0         0         0         0   \n",
       "4       0  ...         0         0         0         0         0         0   \n",
       "\n",
       "   pixel780  pixel781  pixel782  pixel783  \n",
       "0         0         0         0         0  \n",
       "1         0         0         0         0  \n",
       "2         0         0         0         0  \n",
       "3         0         0         0         0  \n",
       "4         0         0         0         0  \n",
       "\n",
       "[5 rows x 784 columns]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1111c6656a0>"
      ]
     },
     "execution_count": 9,
     "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": [
    "train.label.value_counts().plot(kind = \"bar\") # Most of the classes are Balanced"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split the data in train and test and build a model and check the performance\n",
    "from sklearn.model_selection import train_test_split\n",
    "x = train.drop(\"label\", axis = 1)\n",
    "y = train.label\n",
    "\n",
    "xtrain, xtest, ytrain,ytest = train_test_split(x,y, test_size = 0.25, random_state = 100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing Models\n",
    "from catboost import CatBoostClassifier\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier, AdaBoostClassifier\n",
    "from lightgbm import LGBMClassifier\n",
    "from xgboost.sklearn import XGBClassifier\n",
    "from sklearn.metrics import accuracy_score, classification_report, cohen_kappa_score, matthews_corrcoef"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Defining Models\n",
    "cb = CatBoostClassifier()\n",
    "rf = RandomForestClassifier()\n",
    "lg = LogisticRegression()\n",
    "gbm = GradientBoostingClassifier()\n",
    "ada = AdaBoostClassifier()\n",
    "lgbm = LGBMClassifier()\n",
    "xgb = XGBClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Learning rate set to 0.094349\n",
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\mukul\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:762: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n"
     ]
    }
   ],
   "source": [
    "# Fitting all the Models\n",
    "pred_cb = cb.fit(xtrain, ytrain).predict(xtest)\n",
    "pred_rf = rf.fit(xtrain, ytrain).predict(xtest)\n",
    "pred_lg = lg.fit(xtrain, ytrain).predict(xtest)\n",
    "pred_gbm = gbm.fit(xtrain, ytrain).predict(xtest)\n",
    "pred_ada = ada.fit(xtrain, ytrain).predict(xtest)\n",
    "pred_lgbm = lgbm.fit(xtrain, ytrain).predict(xtest)\n",
    "pred_xgb = xgb.fit(xtrain, ytrain).predict(xtest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
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      "application/javascript": [
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       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('import matplotlib.pyplot as plt\\nimport pandas as pd'); }\n",
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       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('import matplotlib.pyplot as plt\\nimport pandas as pd'); }\n",
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       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('import matplotlib.pyplot as plt\\nimport pandas as pd'); }\n",
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       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('import matplotlib.pyplot as plt\\nimport pandas as pd'); }\n",
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       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('import matplotlib.pyplot as plt\\nimport pandas as pd'); }\n",
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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>Model_Name</th>\n",
       "      <th>Accuracy Score</th>\n",
       "      <th>Kappa Score</th>\n",
       "      <th>Matthew Correl</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Catboost Model</td>\n",
       "      <td>0.968190</td>\n",
       "      <td>0.964648</td>\n",
       "      <td>0.964651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>GBM Model</td>\n",
       "      <td>0.943238</td>\n",
       "      <td>0.936917</td>\n",
       "      <td>0.936926</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ADABoost Model</td>\n",
       "      <td>0.742476</td>\n",
       "      <td>0.713679</td>\n",
       "      <td>0.714161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>RF Model</td>\n",
       "      <td>0.963238</td>\n",
       "      <td>0.959145</td>\n",
       "      <td>0.959150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>LogReg Model</td>\n",
       "      <td>0.911429</td>\n",
       "      <td>0.901557</td>\n",
       "      <td>0.901603</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>LGBM Model</td>\n",
       "      <td>0.974095</td>\n",
       "      <td>0.971211</td>\n",
       "      <td>0.971214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>XGB Model</td>\n",
       "      <td>0.973048</td>\n",
       "      <td>0.970046</td>\n",
       "      <td>0.970049</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Model_Name  Accuracy Score  Kappa Score  Matthew Correl\n",
       "0  Catboost Model        0.968190     0.964648        0.964651\n",
       "1       GBM Model        0.943238     0.936917        0.936926\n",
       "2  ADABoost Model        0.742476     0.713679        0.714161\n",
       "3        RF Model        0.963238     0.959145        0.959150\n",
       "4    LogReg Model        0.911429     0.901557        0.901603\n",
       "5      LGBM Model        0.974095     0.971211        0.971214\n",
       "6       XGB Model        0.973048     0.970046        0.970049"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Printing the Metrics of all the Models\n",
    "\n",
    "cols = ['Model_Name', 'Accuracy Score', 'Kappa Score', 'Matthew Correl']\n",
    "\n",
    "# creating a empty dataframe of the colums\n",
    "result_table = pd.DataFrame(columns = cols)\n",
    "\n",
    "# Model\n",
    "cbmodel = pd.Series({'Model_Name': \"Catboost Model\",\n",
    "                     'Accuracy Score': accuracy_score(ytest, pred_cb),\n",
    "                     'Kappa Score': cohen_kappa_score(ytest, pred_cb),\n",
    "                     'Matthew Correl': matthews_corrcoef(ytest, pred_cb)})\n",
    "\n",
    "GBMModel = pd.Series({'Model_Name': \"GBM Model\",\n",
    "                     'Accuracy Score': accuracy_score(ytest, pred_gbm),\n",
    "                     'Kappa Score': cohen_kappa_score(ytest, pred_gbm),\n",
    "                     'Matthew Correl': matthews_corrcoef(ytest, pred_gbm)})\n",
    "RFModel = pd.Series({'Model_Name': \"RF Model\",\n",
    "                     'Accuracy Score': accuracy_score(ytest, pred_rf),\n",
    "                     'Kappa Score': cohen_kappa_score(ytest, pred_rf),\n",
    "                     'Matthew Correl': matthews_corrcoef(ytest, pred_rf)})\n",
    "LGModel = pd.Series({'Model_Name': \"LogReg Model\",\n",
    "                     'Accuracy Score': accuracy_score(ytest, pred_lg),\n",
    "                     'Kappa Score': cohen_kappa_score(ytest, pred_lg),\n",
    "                     'Matthew Correl': matthews_corrcoef(ytest, pred_lg)})\n",
    "LGBMModel = pd.Series({'Model_Name': \"LGBM Model\",\n",
    "                     'Accuracy Score': accuracy_score(ytest, pred_lgbm),\n",
    "                     'Kappa Score': cohen_kappa_score(ytest, pred_lgbm),\n",
    "                     'Matthew Correl': matthews_corrcoef(ytest, pred_lgbm)})\n",
    "XGBModel = pd.Series({'Model_Name': \"XGB Model\",\n",
    "                     'Accuracy Score': accuracy_score(ytest, pred_xgb),\n",
    "                     'Kappa Score': cohen_kappa_score(ytest, pred_xgb),\n",
    "                     'Matthew Correl': matthews_corrcoef(ytest, pred_xgb)})\n",
    "ADAModel = pd.Series({'Model_Name': \"ADABoost Model\",\n",
    "                     'Accuracy Score': accuracy_score(ytest, pred_ada),\n",
    "                     'Kappa Score': cohen_kappa_score(ytest, pred_ada),\n",
    "                     'Matthew Correl': matthews_corrcoef(ytest, pred_ada)})\n",
    "\n",
    "\n",
    "\n",
    "# append the values to the result table\n",
    "result_table = result_table.append(cbmodel, ignore_index = True)\n",
    "result_table = result_table.append(GBMModel, ignore_index = True)\n",
    "result_table = result_table.append(ADAModel, ignore_index = True)\n",
    "result_table = result_table.append(RFModel, ignore_index = True)\n",
    "result_table = result_table.append(LGModel, ignore_index = True)\n",
    "result_table = result_table.append(LGBMModel, ignore_index = True)\n",
    "result_table = result_table.append(XGBModel, ignore_index = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "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>Model_Name</th>\n",
       "      <th>Accuracy Score</th>\n",
       "      <th>Kappa Score</th>\n",
       "      <th>Matthew Correl</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>LGBM Model</td>\n",
       "      <td>0.974095</td>\n",
       "      <td>0.971211</td>\n",
       "      <td>0.971214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>XGB Model</td>\n",
       "      <td>0.973048</td>\n",
       "      <td>0.970046</td>\n",
       "      <td>0.970049</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Catboost Model</td>\n",
       "      <td>0.968190</td>\n",
       "      <td>0.964648</td>\n",
       "      <td>0.964651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>RF Model</td>\n",
       "      <td>0.963238</td>\n",
       "      <td>0.959145</td>\n",
       "      <td>0.959150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>GBM Model</td>\n",
       "      <td>0.943238</td>\n",
       "      <td>0.936917</td>\n",
       "      <td>0.936926</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>LogReg Model</td>\n",
       "      <td>0.911429</td>\n",
       "      <td>0.901557</td>\n",
       "      <td>0.901603</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ADABoost Model</td>\n",
       "      <td>0.742476</td>\n",
       "      <td>0.713679</td>\n",
       "      <td>0.714161</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Model_Name  Accuracy Score  Kappa Score  Matthew Correl\n",
       "5      LGBM Model        0.974095     0.971211        0.971214\n",
       "6       XGB Model        0.973048     0.970046        0.970049\n",
       "0  Catboost Model        0.968190     0.964648        0.964651\n",
       "3        RF Model        0.963238     0.959145        0.959150\n",
       "1       GBM Model        0.943238     0.936917        0.936926\n",
       "4    LogReg Model        0.911429     0.901557        0.901603\n",
       "2  ADABoost Model        0.742476     0.713679        0.714161"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result_table.sort_values(by=\"Kappa Score\", ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('import matplotlib.pyplot as plt\\nimport pandas as pd'); }\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# As Kappa score and Matthew's Correlation Coefficient is very high. We will tune the model as per LGBM and make submission\n",
    "lgbm1 = LGBMClassifier()\n",
    "\n",
    "pred_lgbm = lgbm1.fit(x, y).predict(test)\n",
    "\n",
    "solution = pd.DataFrame({'ImageId':submission.ImageId, \"Label\": pred_lgbm})\n",
    "solution.to_csv(\"LGBM.csv\", index = False) # Kaggle Submission Score: 0.97307 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "C:\\Users\\mukul\n"
     ]
    }
   ],
   "source": [
    "cd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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