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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "sex_pivot= train.pivot_table(index='Sex',values='Survived')\n", + "sex_pivot.plot.bar()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "raw", + "metadata": {}, + "source": [ + "class_pivot= train.pivot_table(index='Pclass',values='Survived')\n", + "class_pivot.plot.bar()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "count 714.000000\n", + "mean 29.699118\n", + "std 14.526497\n", + "min 0.420000\n", + "25% 20.125000\n", + "50% 28.000000\n", + "75% 38.000000\n", + "max 80.000000\n", + "Name: Age, dtype: float64" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "train['Age'].describe()" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "survived= train[train['Survived']==1]\n", + "died= train[train['Survived']==0]\n", + "survived[\"Age\"].plot.hist(alpha=0.5,color='red',bins=50)\n", + "died[\"Age\"].plot.hist(alpha=0.5,color='blue',bins=50)\n", + "plt.legend(['Survived','Died'])\n", + "plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "def process_age(df,cut_points,label_names) :\n", + " df[\"Age\"]= df[\"Age\"].fillna(-0.5)\n", + " df[\"age_categories\"]=pd.cut(df[\"Age\"],cut_points,labels=label_names)\n", + " return df\n", + "\n", + "cut_points=[-1,0,5,12,18,35,60,100]\n", + "label_names=[\"Missing\",\"Infant\",\"Child\",\"Teenager\",\"young adult\",\"adult\",\"senior\"]\n", + "train= process_age(train,cut_points,label_names)\n", + "test= process_age(test,cut_points,label_names)\n", + "\n", + "pivot= train.pivot_table(index=\"age_categories\",values='Survived')\n", + "pivot.plot.bar()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [], + "source": [ + "def create_dummies(df,column_name) :\n", + " dummies= pd.get_dummies(df[column_name],prefix=column_name)\n", + " df= pd.concat([df,dummies],axis=1)\n", + " return df\n", + "for column in [\"Pclass\",\"Sex\",\"age_categories\"]:\n", + " train= create_dummies(train,column)\n", + " test= create_dummies(test,column)" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.linear_model import LogisticRegression\n", + "lr= LogisticRegression()" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\Chaitanya Kaul\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n", + " FutureWarning)\n" + ] + }, + { + "data": { + "text/plain": [ + "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n", + " intercept_scaling=1, l1_ratio=None, max_iter=100,\n", + " multi_class='warn', n_jobs=None, penalty='l2',\n", + " random_state=None, solver='warn', tol=0.0001, verbose=0,\n", + " warm_start=False)" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "columns = ['Pclass_2', 'Pclass_3', 'Sex_male']\n", + "lr.fit(train[columns], train['Survived'])" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\Chaitanya Kaul\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n", + " FutureWarning)\n" + ] + }, + { + "data": { + "text/plain": [ + "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n", + " intercept_scaling=1, l1_ratio=None, max_iter=100,\n", + " multi_class='warn', n_jobs=None, penalty='l2',\n", + " random_state=None, solver='warn', tol=0.0001, verbose=0,\n", + " warm_start=False)" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from sklearn.linear_model import LogisticRegression\n", + "\n", + "columns = ['Pclass_1', 'Pclass_2', 'Pclass_3', 'Sex_female', 'Sex_male',\n", + " 'age_categories_Missing','age_categories_Infant',\n", + " 'age_categories_Child', 'age_categories_Teenager',\n", + " 'age_categories_young adult', 'age_categories_adult',\n", + " 'age_categories_senior']\n", + "\n", + "lr = LogisticRegression()\n", + "lr.fit(train[columns], train[\"Survived\"])" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": {}, + "outputs": [], + "source": [ + "holdout=test\n", + "from sklearn.model_selection import train_test_split\n", + "\n", + "all_X = train[columns]\n", + "all_y = train['Survived']\n", + "\n", + "train_x, test_x, train_y, test_y = train_test_split(\n", + " all_X, all_y, test_size=0.20,random_state=0)" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\Chaitanya Kaul\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n", + " FutureWarning)\n" + ] + } + ], + "source": [ + "lr.fit(train_x,train_y)\n", + "predictions= lr.predict(test_x)" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.metrics import accuracy_score\n", + "accuracy= accuracy_score(test_y,predictions)" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.8100558659217877\n" + ] + } + ], + "source": [ + "print(accuracy)" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\Chaitanya Kaul\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n", + " FutureWarning)\n", + "C:\\Users\\Chaitanya Kaul\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n", + " FutureWarning)\n", + "C:\\Users\\Chaitanya Kaul\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n", + " FutureWarning)\n", + "C:\\Users\\Chaitanya Kaul\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n", + " FutureWarning)\n", + "C:\\Users\\Chaitanya Kaul\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n", + " FutureWarning)\n", + "C:\\Users\\Chaitanya Kaul\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n", + " FutureWarning)\n", + "C:\\Users\\Chaitanya Kaul\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n", + " FutureWarning)\n", + "C:\\Users\\Chaitanya Kaul\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n", + " FutureWarning)\n", + "C:\\Users\\Chaitanya Kaul\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n", + " FutureWarning)\n", + "C:\\Users\\Chaitanya Kaul\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n", + " FutureWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[0.76404494 0.76404494 0.7752809 0.78651685 0.8 0.80681818\n", + " 0.80898876 0.81111111 0.83146067 0.87640449]\n", + "0.8024670865963002\n" + ] + } + ], + "source": [ + "from sklearn.model_selection import cross_val_score\n", + "\n", + "lr= LogisticRegression()\n", + "scores = cross_val_score(lr,all_X,all_y,cv=10)\n", + "scores.sort()\n", + "accuracy= scores.mean()\n", + "print(scores)\n", + "print(accuracy)" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\Chaitanya Kaul\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n", + " FutureWarning)\n" + ] + } + ], + "source": [ + "lr= LogisticRegression()\n", + "lr.fit(all_X,all_y)\n", + "holdout_predictions= lr.predict(holdout[columns])" + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "metadata": {}, + "outputs": [], + "source": [ + "holdout_ids= holdout['PassengerId']\n", + "submission_df= {\"PassengerId\" : holdout_ids,\n", + " \"Survived\":holdout_predictions}\n", + "submission= pd.DataFrame(submission_df)" + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "metadata": {}, + "outputs": [], + "source": [ + "submission.to_csv(\"submission.csv\",index=False)" + ] + }, + { + "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": "3.7.4" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +}