{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"pygments_lexer":"ipython3","nbconvert_exporter":"python","version":"3.6.4","file_extension":".py","codemirror_mode":{"name":"ipython","version":3},"name":"python","mimetype":"text/x-python"}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"

Titanic: Machine Learning model

\nName: Eliran Malichy \nlink: https://www.kaggle.com/eliranmalichy \n","metadata":{}},{"cell_type":"markdown","source":"In this exercise I'm going to predict who is going to survive the Titanic disaster by using the available dataset. \n In order to do that I'm going to: \n* read the data file. \n* use data cleansing. \n* analyze to data. \n* create parameters and hyper parameters. \n* create a logistic regression model. \nAssumption: the people with the most chance to survive are: women, children and first-class passengers. \n\n","metadata":{}},{"cell_type":"code","source":"#!pip install --upgrade plotly\n#!pip install sweetviz","metadata":{"execution":{"iopub.status.busy":"2022-05-29T16:29:49.673945Z","iopub.execute_input":"2022-05-29T16:29:49.674684Z","iopub.status.idle":"2022-05-29T16:29:49.705083Z","shell.execute_reply.started":"2022-05-29T16:29:49.674531Z","shell.execute_reply":"2022-05-29T16:29:49.704334Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"## imports\nimport numpy as np\nimport pandas as pd\nimport seaborn as sns\nimport matplotlib.pyplot as plt\nimport random as rnd\nimport os\nimport seaborn as sns\nimport sklearn\nimport plotly.graph_objects as go\nimport plotly.express as px\n\nfrom tqdm.auto import tqdm\nfrom sklearn.metrics import mean_squared_error\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn.svm import SVC, LinearSVC\nfrom sklearn.ensemble import RandomForestClassifier\nfrom sklearn.neighbors import KNeighborsClassifier\nfrom sklearn.naive_bayes import GaussianNB\nfrom sklearn.linear_model import Perceptron\nfrom sklearn.tree import DecisionTreeClassifier\nfrom sklearn.metrics import classification_report,accuracy_score\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.preprocessing import StandardScaler\nfrom sklearn.compose import ColumnTransformer\nfrom sklearn.preprocessing import OrdinalEncoder\nfrom sklearn.feature_selection import RFECV\nfrom sklearn.linear_model import SGDRegressor\nfrom sklearn.model_selection import RepeatedKFold\nfrom sklearn.neighbors import KNeighborsClassifier\nfrom sklearn.model_selection import train_test_split #for split the data\nfrom sklearn.metrics import accuracy_score #for accuracy_score\nfrom sklearn.model_selection import KFold #for K-fold cross validation\nfrom sklearn.model_selection import cross_val_score #score evaluation\nfrom sklearn.model_selection import cross_val_predict #prediction\nfrom sklearn.metrics import confusion_matrix #for confusion matrix\nfrom sklearn.naive_bayes import GaussianNB\nfrom sklearn.discriminant_analysis import LinearDiscriminantAnalysis\nfrom sklearn.preprocessing import OneHotEncoder\nfrom sklearn.pipeline import make_pipeline\nfrom sklearn.preprocessing import PolynomialFeatures\n\nfor dirname, _, filenames in os.walk('/kaggle/input'):\n for filename in filenames:\n print(os.path.join(dirname, filename))","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","execution":{"iopub.status.busy":"2022-05-29T16:29:49.707270Z","iopub.execute_input":"2022-05-29T16:29:49.707989Z","iopub.status.idle":"2022-05-29T16:29:53.092277Z","shell.execute_reply.started":"2022-05-29T16:29:49.707957Z","shell.execute_reply":"2022-05-29T16:29:53.091069Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"## functions\n\ndef printL(str):\n print(str,\"\\n\\n\")\n \ndef replace_titles_to_rare(dataset):\n dataset['Title'] = dataset['Title'].replace(['Lady', 'Countess','Capt', 'Col','Don', 'Dr', 'Major', 'Rev', 'Sir', 'Jonkheer', 'Dona'], 'Rare')\n \ndef replace_titles(dataset):\n dataset['Title'] = dataset['Title'].replace('Mlle', 'Miss')\n dataset['Title'] = dataset['Title'].replace('Ms', 'Miss')\n dataset['Title'] = dataset['Title'].replace('Mme', 'Mrs')\n\ndef drop_ticket(dataset):\n dataset = dataset.drop('Ticket', axis=1)\n return dataset\n\ndef create_hyper_fare_Pclass(dataset):\n return dataset['Fare']/dataset['Pclass']\n\ndef create_hyper_age_range(dataset):\n if(dataset['Age'] <= 16):\n return 0\n if(dataset['Age'] > 16) & (dataset['Age'] <= 32):\n return 1\n if (dataset['Age'] > 32) & (dataset['Age'] <= 48):\n return 2\n if (dataset['Age'] > 48) & (dataset['Age'] <= 64):\n return 3\n if (dataset['Age'] > 64):\n return 4\n \ndef extract_title(dataset):\n dataset['Title'] = dataset.Name.str.extract(' ([A-Za-z]+)\\.', expand=False)\n\ndef convert_categorical_titles_to_ordinal(dataset,mapping):\n dataset['Title'] = dataset['Title'].map(mapping)\n dataset['Title'] = dataset['Title'].fillna(0)\n\ndef convert_sex_strings_to_numerical(dataset):\n dataset['Sex'] = dataset['Sex'].map( {'female': 1, 'male': 0} ).astype(int)\n \ndef guess_values_age_Pclass(dataset):\n guess_ages = np.zeros((2,3))\n for i in range(0, 2):\n for j in range(0, 3):\n guess_df = dataset[(dataset['Sex'] == i) & \\\n (dataset['Pclass'] == j+1)]['Age'].dropna()\n age_guess = guess_df.median()\n guess_ages[i,j] = int( age_guess/0.5 + 0.5 ) * 0.5 \n for i in range(0, 2):\n for j in range(0, 3):\n dataset.loc[ (dataset.Age.isnull()) & (dataset.Sex == i) & (dataset.Pclass == j+1),\\\n 'Age'] = guess_ages[i,j]\n dataset['Age'] = dataset['Age'].astype(int) \n \ndef process_family(df):\n # introducing a new feature : the size of families (including the passenger)\n df['FamilySize'] = df['Parch'] + df['SibSp'] + 1\n # introducing other features based on the family size\n df['Singleton'] = df['FamilySize'].map(lambda s: 1 if s == 1 else 0)\n df['SmallFamily'] = df['FamilySize'].map(lambda s: 1 if 2 <= s <= 4 else 0)\n df['LargeFamily'] = df['FamilySize'].map(lambda s: 1 if 5 <= s else 0) \n return df\n\ndef create_heatmap_graph(dataset):\n plt.figure(figsize=(13,13))\n cor = np.abs(dataset.corr())\n sns.heatmap(cor, annot=True, cmap=plt.cm.Blues, vmin=-1, vmax=1)\n plt.show()\n \ndef create_grid(col,row,feature):\n grid = sns.FacetGrid(data_train, col = col, row = row, height=2.2, aspect=1.6)\n grid.map(plt.hist, feature, bins=20)\n \ndef process_cabin(df): \n cabin_dic={}\n count=0\n # replacing missing cabins with U (for Uknown)\n df.Cabin.fillna('T', inplace=True)\n # mapping each Cabin value with the cabin letter\n df['Cabin'] = df['Cabin'].map(lambda c: c[0])\n for letter in df['Cabin']:\n letter_num = cabin_dic.get(letter)\n if(letter_num==None):\n cabin_dic[letter] = count\n count=count+1\n df['Cabin'] = df['Cabin'].map(cabin_dic).astype(int)\n return df\n\ndef check_for_empty_values(data_train,data_test):\n ## check if there are missing values\n print(\"Data training:\\n\")\n printL(data_train.isna().any())\n print(\"Data test:\\n\")\n printL(data_test.isna().any())\n \ndef refill_empty_values(dataset,freq_port): \n guess_values_age_Pclass(dataset)\n dataset['Embarked'] = dataset['Embarked'].fillna(freq_port)\n dataset['Embarked'] = dataset['Embarked'].map( {'S': 0, 'C': 1, 'Q': 2} ).astype(int)\n dataset=process_cabin(dataset)\n dataset = drop_ticket(dataset) \n return dataset\n \ndef add_hypers(dataset):\n dataset['Fare_divided_by_Pclass'] = dataset.apply(create_hyper_fare_Pclass, axis = 1)\n dataset['Age_range'] = dataset.apply(create_hyper_age_range, axis = 1)\n dataset=process_family(dataset)\n return dataset \n\ndef get_cv_score_and_loss(X, t, model, transformer=None, k=None, p=None, show_score_loss_graphs=False, use_pbar=True):\n scores_losses_df = pd.DataFrame(columns=['fold_id', 'split', 'score', 'loss'])\n\n if k is not None:\n cv = KFold(n_splits=k, shuffle=True, random_state=42)\n elif p is not None:\n cv = LeavePOut(p)\n else:\n raise ValueError('you need to specify k or p in order for the cv to work')\n\n if use_pbar:\n pbar = tqdm(desc='Computing Models',\n total=find_generator_len(cv.split(X)))\n\n for i, (train_ids, val_ids) in enumerate(cv.split(X)):\n X_train = X.loc[train_ids]\n t_train = t.loc[train_ids]\n X_val = X.loc[val_ids]\n t_val = t.loc[val_ids]\n\n model.fit(X_train, t_train)\n\n y_train = model.predict(X_train)\n y_val = model.predict(X_val)\n scores_losses_df.loc[len(scores_losses_df)] =\\\n [i, 'train', model.score(X_train, t_train),\n mean_squared_error(t_train, y_train)]\n scores_losses_df.loc[len(scores_losses_df)] =\\\n [i, 'val', model.score(X_val, t_val), mean_squared_error(t_val, y_val)]\n\n if use_pbar:\n pbar.update()\n\n if use_pbar:\n pbar.close()\n\n val_scores_losses_df = scores_losses_df[scores_losses_df['split']=='val']\n train_scores_losses_df = scores_losses_df[scores_losses_df['split']=='train']\n\n mean_val_score = val_scores_losses_df['score'].mean()\n mean_val_loss = val_scores_losses_df['loss'].mean()\n mean_train_score = train_scores_losses_df['score'].mean()\n mean_train_loss = train_scores_losses_df['loss'].mean()\n\n if show_score_loss_graphs:\n fig = px.line(scores_losses_df, x='fold_id', y='score', color='split', title=f'Mean Val Score: {mean_val_score:.2f}, Mean Train Score: {mean_train_score:.2f}')\n fig.show()\n fig = px.line(scores_losses_df, x='fold_id', y='loss', color='split', title=f'Mean Val Loss: {mean_val_loss:.2f}, Mean Train Loss: {mean_train_loss:.2f}')\n fig.show()\n\n return mean_val_score, mean_val_loss,mean_train_score, mean_train_loss\n\ndef evaulate(x,y,model,model_name):\n val_score, val_loss, train_score, train_loss = get_cv_score_and_loss(x, y, model, k=30, show_score_loss_graphs=True)\n print(\"Model: \",model_name)\n print(f'Mean cv val score: {val_score:.2f}\\nMean cv val loss {val_loss:.2f}')\n print(f'Mean cv train val score: {train_score:.2f}\\nMean cv train val loss {train_loss:.2f}')\n \ndef rec_feat_selection(X, t):\n numerical_cols = X.select_dtypes(include=['int64', 'float64']).columns\n categorical_cols = X.select_dtypes(include=['object', 'bool']).columns\n all_cols = list(categorical_cols) + list(numerical_cols)\n ct_enc_std = ColumnTransformer([\n (\"encoding\", OrdinalEncoder(), categorical_cols),\n (\"standard\", StandardScaler(), numerical_cols)])\n X_encoded = pd.DataFrame(ct_enc_std.fit_transform(X, t), columns=all_cols)\n\n selector = RFECV(SGDRegressor(random_state=1), cv=RepeatedKFold(n_splits=5, n_repeats=10, random_state=1)).fit(X_encoded, t)\n display(X_encoded.loc[:, selector.support_])\n\n fig = go.Figure()\n results = selector.cv_results_['mean_test_score'] # Getting the mean cv score for each set of features\n fig.add_trace(go.Scatter(x=[i for i in range(1, len(results) + 1)], y=results))\n fig.update_xaxes(title_text=\"Number of features selected\")\n fig.update_yaxes(title_text=\"Cross validation score (nb of correct classifications)\")\n fig.show()\n\n return X_encoded.loc[:, selector.support_] \n\ndef find_generator_len(generator, use_pbar=True):\n i = 0\n \n if use_pbar:\n pbar = tqdm(desc='Calculating Length',\n ncols=1000,\n bar_format='{desc}{bar:10}{r_bar}')\n\n for a in generator:\n i += 1\n\n if use_pbar:\n pbar.update()\n\n if use_pbar:\n pbar.close()\n\n return i \n\ndef check_model(model,X_train,y_train,X_test,model_name):\n model.fit(X_train,y_train)\n prediction=model.predict(X_test)\n print('--------------The Accuracy of the', model_name,'----------------------------')\n print('The accuracy is',round(accuracy_score(prediction,y_test)*100,2))\n kfold = KFold(n_splits=10, random_state=42, shuffle=True) # k=10, split the data into 10 equal parts\n result_model=cross_val_score(model,x_train_all,y_train_all,cv=kfold,scoring='accuracy')\n print('The cross validated score is:',round(result_model.mean()*100,2))\n y_pred = cross_val_predict(model,x_train_all,y_train_all,cv=kfold)\n sns.heatmap(confusion_matrix(y_train_all,y_pred),annot=True,fmt='3.0f',cmap=\"summer\")\n plt.title('Confusion_matrix', y=1.05, size=15)\n \n# show graph of score and loss by plynomial degree of numerical features\ndef show_degree_graphs_cv_train(X, t, model, k=None, p=None, max_degree=10):\n numerical_cols = X.select_dtypes(include=['int64', 'float64']).columns\n categorical_cols = X.select_dtypes(include=['object', 'bool']).columns\n ct = ColumnTransformer([\n (\"encoding\", OneHotEncoder(sparse=False, handle_unknown='ignore'), categorical_cols),\n (\"standard\", StandardScaler(), numerical_cols)])\n \n val_train_score_loss_df = pd.DataFrame(columns=['degree', 'split', 'score', 'loss'])\n for i in tqdm(range(1, max_degree), desc='Poly Degree'):\n ct_enc_std_poly = ColumnTransformer([\n (\"encoding\", OneHotEncoder(sparse=False, handle_unknown='ignore'), categorical_cols),\n (\"standard_poly\", make_pipeline(PolynomialFeatures(degree=i), StandardScaler()), numerical_cols)])\n model_pipe = make_pipeline(ct_enc_std_poly, model)\n val_score, val_loss, train_score, train_loss = get_cv_score_and_loss(X, t, model_pipe, transformer=ct, k=k, p=p, show_score_loss_graphs=False, use_pbar=False)\n val_train_score_loss_df.loc[len(val_train_score_loss_df)] = [i, 'train', train_score, train_loss]\n val_train_score_loss_df.loc[len(val_train_score_loss_df)] = [i, 'cv', val_score, val_loss]\n\n fig = px.line(val_train_score_loss_df, x='degree', y='score', color='split')\n fig.show()\n fig = px.line(val_train_score_loss_df, x='degree', y='loss', color='split')\n fig.show()\n\n ","metadata":{"execution":{"iopub.status.busy":"2022-05-29T16:29:53.094132Z","iopub.execute_input":"2022-05-29T16:29:53.094455Z","iopub.status.idle":"2022-05-29T16:29:53.152269Z","shell.execute_reply.started":"2022-05-29T16:29:53.094420Z","shell.execute_reply":"2022-05-29T16:29:53.150937Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"## get and copy data\ndata_train = pd.read_csv('../input/titanic/train.csv')\ndata_test = pd.read_csv('../input/titanic/test.csv')\ndata_test_temp = data_test.copy()\ndata_train_temp = data_train.copy()\ndata_test_passenger_id= data_test_temp['PassengerId']","metadata":{"execution":{"iopub.status.busy":"2022-05-29T16:29:53.155097Z","iopub.execute_input":"2022-05-29T16:29:53.155388Z","iopub.status.idle":"2022-05-29T16:29:53.197869Z","shell.execute_reply.started":"2022-05-29T16:29:53.155359Z","shell.execute_reply":"2022-05-29T16:29:53.197190Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Essential data analysis ","metadata":{}},{"cell_type":"code","source":"display(data_train_temp.head())","metadata":{"execution":{"iopub.status.busy":"2022-05-29T16:29:53.198981Z","iopub.execute_input":"2022-05-29T16:29:53.199865Z","iopub.status.idle":"2022-05-29T16:29:53.225741Z","shell.execute_reply.started":"2022-05-29T16:29:53.199810Z","shell.execute_reply":"2022-05-29T16:29:53.225275Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"## check data for unique values \ndata_train.describe(include=['O'])","metadata":{"execution":{"iopub.status.busy":"2022-05-29T16:29:53.226733Z","iopub.execute_input":"2022-05-29T16:29:53.227028Z","iopub.status.idle":"2022-05-29T16:29:53.260159Z","shell.execute_reply.started":"2022-05-29T16:29:53.226996Z","shell.execute_reply":"2022-05-29T16:29:53.258911Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"## check for data type\nprint(\"Data training:\\n\")\nprintL(data_train.info())\nprint(\"Data test:\\n\")\nprintL(data_test.info())","metadata":{"execution":{"iopub.status.busy":"2022-05-29T16:29:53.261427Z","iopub.execute_input":"2022-05-29T16:29:53.261688Z","iopub.status.idle":"2022-05-29T16:29:53.290803Z","shell.execute_reply.started":"2022-05-29T16:29:53.261655Z","shell.execute_reply":"2022-05-29T16:29:53.289483Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"convert_sex_strings_to_numerical(data_train_temp)\nconvert_sex_strings_to_numerical(data_test_temp)","metadata":{"execution":{"iopub.status.busy":"2022-05-29T16:29:53.292395Z","iopub.execute_input":"2022-05-29T16:29:53.292656Z","iopub.status.idle":"2022-05-29T16:29:53.299763Z","shell.execute_reply.started":"2022-05-29T16:29:53.292624Z","shell.execute_reply":"2022-05-29T16:29:53.299047Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"check_for_empty_values(data_train_temp,data_test_temp)","metadata":{"execution":{"iopub.status.busy":"2022-05-29T16:29:53.300776Z","iopub.execute_input":"2022-05-29T16:29:53.301537Z","iopub.status.idle":"2022-05-29T16:29:53.317410Z","shell.execute_reply.started":"2022-05-29T16:29:53.301506Z","shell.execute_reply":"2022-05-29T16:29:53.316411Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"## Data Cleansing\nfreq_port = data_train_temp.Embarked.dropna().mode()[0]\ndata_train_temp = refill_empty_values(data_train_temp,freq_port)\ndata_test_temp = refill_empty_values(data_test_temp,freq_port)\ndata_test_temp['Fare'].fillna(data_test_temp['Fare'].dropna().median(), inplace=True)","metadata":{"execution":{"iopub.status.busy":"2022-05-29T16:29:53.319830Z","iopub.execute_input":"2022-05-29T16:29:53.320043Z","iopub.status.idle":"2022-05-29T16:29:53.372569Z","shell.execute_reply.started":"2022-05-29T16:29:53.320016Z","shell.execute_reply":"2022-05-29T16:29:53.371590Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"check_for_empty_values(data_train_temp,data_test_temp)","metadata":{"execution":{"iopub.status.busy":"2022-05-29T16:29:53.374109Z","iopub.execute_input":"2022-05-29T16:29:53.374421Z","iopub.status.idle":"2022-05-29T16:29:53.387224Z","shell.execute_reply.started":"2022-05-29T16:29:53.374383Z","shell.execute_reply":"2022-05-29T16:29:53.386122Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"## Analyze Pclass feature with survived\ndisplay(data_train[['Pclass', 'Survived']].groupby(['Pclass'], as_index=False).mean().sort_values(by='Survived', ascending=False))\n## Analyze sex feature with survived\ndisplay(data_train[[\"Sex\", \"Survived\"]].groupby(['Sex'], as_index=False).mean().sort_values(by='Survived', ascending=False))\n## Analyze SibSp feature with survived\ndisplay(data_train[[\"SibSp\", \"Survived\"]].groupby(['SibSp'], as_index=False).mean().sort_values(by='Survived', ascending=False))\n## Analyze Parch feature with survived\ndisplay(data_train[[\"Parch\", \"Survived\"]].groupby(['Parch'], as_index=False).mean().sort_values(by='Survived', ascending=False))","metadata":{"execution":{"iopub.status.busy":"2022-05-29T16:29:53.388637Z","iopub.execute_input":"2022-05-29T16:29:53.389415Z","iopub.status.idle":"2022-05-29T16:29:53.436616Z","shell.execute_reply.started":"2022-05-29T16:29:53.389378Z","shell.execute_reply":"2022-05-29T16:29:53.436093Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Graphs: ","metadata":{}},{"cell_type":"code","source":"create_heatmap_graph(data_train_temp)","metadata":{"execution":{"iopub.status.busy":"2022-05-29T16:29:53.437659Z","iopub.execute_input":"2022-05-29T16:29:53.437950Z","iopub.status.idle":"2022-05-29T16:29:54.147096Z","shell.execute_reply.started":"2022-05-29T16:29:53.437922Z","shell.execute_reply":"2022-05-29T16:29:54.146027Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"create_grid('Survived',None,'Age')","metadata":{"execution":{"iopub.status.busy":"2022-05-29T16:29:54.148585Z","iopub.execute_input":"2022-05-29T16:29:54.148894Z","iopub.status.idle":"2022-05-29T16:29:54.584230Z","shell.execute_reply.started":"2022-05-29T16:29:54.148854Z","shell.execute_reply":"2022-05-29T16:29:54.583147Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"create_grid('Survived','Pclass','Age')","metadata":{"execution":{"iopub.status.busy":"2022-05-29T16:29:54.586031Z","iopub.execute_input":"2022-05-29T16:29:54.586362Z","iopub.status.idle":"2022-05-29T16:29:55.832126Z","shell.execute_reply.started":"2022-05-29T16:29:54.586328Z","shell.execute_reply":"2022-05-29T16:29:55.831150Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"create_grid('Sex','Pclass','Age')","metadata":{"execution":{"iopub.status.busy":"2022-05-29T16:29:55.833842Z","iopub.execute_input":"2022-05-29T16:29:55.834179Z","iopub.status.idle":"2022-05-29T16:29:57.354052Z","shell.execute_reply.started":"2022-05-29T16:29:55.834113Z","shell.execute_reply":"2022-05-29T16:29:57.353391Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"grid = sns.FacetGrid(data_train, row='Embarked', height=2.2, aspect=1.6)\ngrid.map(sns.pointplot, 'Pclass', 'Survived', 'Sex', palette='deep',order = [1,2,3], hue_order = ['male','female'])\ngrid.add_legend();","metadata":{"execution":{"iopub.status.busy":"2022-05-29T16:29:57.355413Z","iopub.execute_input":"2022-05-29T16:29:57.356367Z","iopub.status.idle":"2022-05-29T16:29:58.725936Z","shell.execute_reply.started":"2022-05-29T16:29:57.356307Z","shell.execute_reply":"2022-05-29T16:29:58.725366Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"grid = sns.FacetGrid(data_train, row='Embarked', col='Survived', height=2.2, aspect=1.6)\ngrid.map(sns.barplot, 'Sex', 'Fare', ci=None,order = ['male','female'])\ngrid.add_legend();","metadata":{"execution":{"iopub.status.busy":"2022-05-29T16:29:58.727075Z","iopub.execute_input":"2022-05-29T16:29:58.727343Z","iopub.status.idle":"2022-05-29T16:29:59.745294Z","shell.execute_reply.started":"2022-05-29T16:29:58.727313Z","shell.execute_reply":"2022-05-29T16:29:59.744079Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Creating parameters: ","metadata":{}},{"cell_type":"code","source":"extract_title(data_train_temp)\nextract_title(data_test_temp)\npd.crosstab(data_train_temp['Title'], data_train_temp['Sex'])","metadata":{"execution":{"iopub.status.busy":"2022-05-29T16:29:59.746767Z","iopub.execute_input":"2022-05-29T16:29:59.747063Z","iopub.status.idle":"2022-05-29T16:29:59.779165Z","shell.execute_reply.started":"2022-05-29T16:29:59.747021Z","shell.execute_reply":"2022-05-29T16:29:59.778129Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"replace_titles_to_rare(data_train_temp)\nreplace_titles_to_rare(data_test_temp)\nreplace_titles(data_train_temp)\nreplace_titles(data_test_temp)\n\ndata_train_temp[['Title', 'Survived']].groupby(['Title'], as_index=False).mean()","metadata":{"execution":{"iopub.status.busy":"2022-05-29T16:29:59.780796Z","iopub.execute_input":"2022-05-29T16:29:59.781076Z","iopub.status.idle":"2022-05-29T16:29:59.801512Z","shell.execute_reply.started":"2022-05-29T16:29:59.781038Z","shell.execute_reply":"2022-05-29T16:29:59.800785Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"title_mapping = {\"Mr\": 1, \"Miss\": 2, \"Mrs\": 3, \"Master\": 4, \"Rare\": 5}\nconvert_categorical_titles_to_ordinal(data_train_temp,title_mapping) \nconvert_categorical_titles_to_ordinal(data_test_temp,title_mapping) \ndata_train_temp.head()","metadata":{"execution":{"iopub.status.busy":"2022-05-29T16:29:59.803054Z","iopub.execute_input":"2022-05-29T16:29:59.803406Z","iopub.status.idle":"2022-05-29T16:29:59.825249Z","shell.execute_reply.started":"2022-05-29T16:29:59.803368Z","shell.execute_reply":"2022-05-29T16:29:59.824129Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"data_train_temp = data_train_temp.drop(['Name', 'PassengerId'], axis=1)\ndata_test_temp = data_test_temp.drop(['Name'], axis=1)","metadata":{"execution":{"iopub.status.busy":"2022-05-29T16:29:59.827226Z","iopub.execute_input":"2022-05-29T16:29:59.827804Z","iopub.status.idle":"2022-05-29T16:29:59.834480Z","shell.execute_reply.started":"2022-05-29T16:29:59.827769Z","shell.execute_reply":"2022-05-29T16:29:59.833682Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"## creating hyper parametares\nadd_hypers(data_train_temp)\nadd_hypers(data_test_temp)\ndata_train_temp","metadata":{"execution":{"iopub.status.busy":"2022-05-29T16:29:59.835625Z","iopub.execute_input":"2022-05-29T16:29:59.836760Z","iopub.status.idle":"2022-05-29T16:29:59.915225Z","shell.execute_reply.started":"2022-05-29T16:29:59.836702Z","shell.execute_reply":"2022-05-29T16:29:59.914487Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"create_heatmap_graph(data_train_temp)","metadata":{"execution":{"iopub.status.busy":"2022-05-29T16:29:59.916451Z","iopub.execute_input":"2022-05-29T16:29:59.916716Z","iopub.status.idle":"2022-05-29T16:30:00.905009Z","shell.execute_reply.started":"2022-05-29T16:29:59.916680Z","shell.execute_reply":"2022-05-29T16:30:00.903925Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Summary: \nBy analyzing the dataset with graph and grids, I saw that my assumption is correct. \nI created the following hyper parameters: \n* Family size classification \n* Age classification \n* Connection between the ticket and the price\n \n","metadata":{}},{"cell_type":"markdown","source":"![leaderboard.jpg](attachment:84c76efa-a1b2-4572-95c6-681613e2afc2.jpg)","metadata":{},"attachments":{"84c76efa-a1b2-4572-95c6-681613e2afc2.jpg":{"image/jpeg":"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fg8OT4b8cf8AgDZ//JdA/Zy7H2/RXxD/AMPdPg//ANC344H/AG42f/yXSf8AD3X4Pcf8U344/wDACz/+S6Q/Zz7H2/RXxD/w90+D3/Qt+OP/AABs/wD5LoH/AAVy+D5/5lvxx/4A2f8A8l0XD2U+x9vUV8Rf8Pcfg/8A9C343/8AAGz/APkuj/h7j8H/APoW/G//AIA2f/yXRdD9jPsfbtFfEf8Aw9v+EHT/AIRvxv6f8eNn/wDJdO/4e2/CD/oW/G//AIA2f/yVRdC9lPsf/9k="}}},{"cell_type":"markdown","source":"Exercise 3: \n","metadata":{}},{"cell_type":"markdown","source":"In this exercise I'm going to try to improve my score from exercise 1, by doing to the following things: \n* Create models. \n* Evaulate models. \n* Finding the best subset of features for this dataset. \n* Finding the best hyper parameters for the chosen model. \n\nRemarks: I understand from the instruction of this exercise, that it is allowed to use only the following models: KNN, NBC or LDA. \n\n","metadata":{}},{"cell_type":"code","source":"x_train_all = data_train_temp.drop([\"Survived\",\"SibSp\",\"Parch\"], axis=1)\ny_train_all = data_train_temp[\"Survived\"]\nx_test_final = data_test_temp.drop([\"PassengerId\",\"SibSp\",\"Parch\"], axis=1).copy()\nX_train,X_test,y_train,y_test = train_test_split(x_train_all, y_train_all, test_size=0.3, random_state=42)\nX_train.shape,X_test.shape,y_train.shape,y_test.shape","metadata":{"execution":{"iopub.status.busy":"2022-05-29T16:30:00.906407Z","iopub.execute_input":"2022-05-29T16:30:00.906686Z","iopub.status.idle":"2022-05-29T16:30:00.921448Z","shell.execute_reply.started":"2022-05-29T16:30:00.906650Z","shell.execute_reply":"2022-05-29T16:30:00.920310Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Create models","metadata":{}},{"cell_type":"code","source":"KNN_model = KNeighborsClassifier(n_neighbors = 4)\n\nNBC_model= GaussianNB()\n\nLDA_model= LinearDiscriminantAnalysis()","metadata":{"execution":{"iopub.status.busy":"2022-05-29T16:30:00.923094Z","iopub.execute_input":"2022-05-29T16:30:00.925392Z","iopub.status.idle":"2022-05-29T16:30:00.929993Z","shell.execute_reply.started":"2022-05-29T16:30:00.925337Z","shell.execute_reply":"2022-05-29T16:30:00.929412Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Evaulate models","metadata":{}},{"cell_type":"markdown","source":"# KNN model","metadata":{}},{"cell_type":"code","source":"check_model(KNN_model,X_train,y_train,X_test,'KNN model')","metadata":{"execution":{"iopub.status.busy":"2022-05-29T16:30:00.931448Z","iopub.execute_input":"2022-05-29T16:30:00.932096Z","iopub.status.idle":"2022-05-29T16:30:01.281897Z","shell.execute_reply.started":"2022-05-29T16:30:00.932028Z","shell.execute_reply":"2022-05-29T16:30:01.280762Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"evaulate(x_train_all, y_train_all,KNN_model,\"KNN model\")","metadata":{"execution":{"iopub.status.busy":"2022-05-29T16:30:01.284742Z","iopub.execute_input":"2022-05-29T16:30:01.284942Z","iopub.status.idle":"2022-05-29T16:30:04.972533Z","shell.execute_reply.started":"2022-05-29T16:30:01.284918Z","shell.execute_reply":"2022-05-29T16:30:04.971618Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"show_degree_graphs_cv_train(x_train_all, y_train_all,KNN_model, k=5 ,max_degree=8) ","metadata":{"execution":{"iopub.status.busy":"2022-05-29T16:30:04.974929Z","iopub.execute_input":"2022-05-29T16:30:04.975230Z","iopub.status.idle":"2022-05-29T16:30:45.779351Z","shell.execute_reply.started":"2022-05-29T16:30:04.975191Z","shell.execute_reply":"2022-05-29T16:30:45.777946Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# NBC model","metadata":{}},{"cell_type":"code","source":"check_model(NBC_model,X_train,y_train,X_test,'NBC model')","metadata":{"execution":{"iopub.status.busy":"2022-05-29T16:30:45.780852Z","iopub.execute_input":"2022-05-29T16:30:45.781121Z","iopub.status.idle":"2022-05-29T16:30:46.072898Z","shell.execute_reply.started":"2022-05-29T16:30:45.781076Z","shell.execute_reply":"2022-05-29T16:30:46.072176Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"evaulate(x_train_all, y_train_all,NBC_model,\"NBC model\")","metadata":{"execution":{"iopub.status.busy":"2022-05-29T16:30:46.074187Z","iopub.execute_input":"2022-05-29T16:30:46.075082Z","iopub.status.idle":"2022-05-29T16:30:46.704864Z","shell.execute_reply.started":"2022-05-29T16:30:46.075031Z","shell.execute_reply":"2022-05-29T16:30:46.703493Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"show_degree_graphs_cv_train(x_train_all, y_train_all,NBC_model, k=5 ,max_degree=8) ","metadata":{"execution":{"iopub.status.busy":"2022-05-29T16:30:46.706290Z","iopub.execute_input":"2022-05-29T16:30:46.706544Z","iopub.status.idle":"2022-05-29T16:31:11.252758Z","shell.execute_reply.started":"2022-05-29T16:30:46.706512Z","shell.execute_reply":"2022-05-29T16:31:11.252111Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# LDA model","metadata":{}},{"cell_type":"code","source":"check_model(LDA_model,X_train,y_train,X_test,'LDA model')","metadata":{"execution":{"iopub.status.busy":"2022-05-29T16:31:11.253716Z","iopub.execute_input":"2022-05-29T16:31:11.253946Z","iopub.status.idle":"2022-05-29T16:31:11.651855Z","shell.execute_reply.started":"2022-05-29T16:31:11.253914Z","shell.execute_reply":"2022-05-29T16:31:11.650308Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"evaulate(x_train_all, y_train_all,LDA_model,\"LDA model\")","metadata":{"execution":{"iopub.status.busy":"2022-05-29T16:31:11.653166Z","iopub.execute_input":"2022-05-29T16:31:11.653392Z","iopub.status.idle":"2022-05-29T16:31:13.057526Z","shell.execute_reply.started":"2022-05-29T16:31:11.653360Z","shell.execute_reply":"2022-05-29T16:31:13.056803Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"show_degree_graphs_cv_train(x_train_all, y_train_all,LDA_model, k=5 ,max_degree=8) ","metadata":{"execution":{"iopub.status.busy":"2022-05-29T16:31:13.058844Z","iopub.execute_input":"2022-05-29T16:31:13.059547Z","iopub.status.idle":"2022-05-29T16:33:37.006922Z","shell.execute_reply.started":"2022-05-29T16:31:13.059507Z","shell.execute_reply":"2022-05-29T16:33:37.006291Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"I chose to use the LDA model because it's got the best accuracy and validation results","metadata":{}},{"cell_type":"markdown","source":"Finding the best subset of features for this dataset","metadata":{"execution":{"iopub.status.busy":"2022-05-26T18:36:56.284947Z","iopub.execute_input":"2022-05-26T18:36:56.285393Z","iopub.status.idle":"2022-05-26T18:36:56.291686Z","shell.execute_reply.started":"2022-05-26T18:36:56.285348Z","shell.execute_reply":"2022-05-26T18:36:56.290349Z"}}},{"cell_type":"code","source":"best_feats = rec_feat_selection(x_train_all, y_train_all).columns","metadata":{"execution":{"iopub.status.busy":"2022-05-29T16:33:37.007905Z","iopub.execute_input":"2022-05-29T16:33:37.008951Z","iopub.status.idle":"2022-05-29T16:33:37.971357Z","shell.execute_reply.started":"2022-05-29T16:33:37.008912Z","shell.execute_reply":"2022-05-29T16:33:37.970325Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"check_model(LDA_model,X_train[best_feats],y_train,X_test[best_feats],'LDA model - selected features')","metadata":{"execution":{"iopub.status.busy":"2022-05-29T16:33:37.972540Z","iopub.execute_input":"2022-05-29T16:33:37.972747Z","iopub.status.idle":"2022-05-29T16:33:38.348965Z","shell.execute_reply.started":"2022-05-29T16:33:37.972724Z","shell.execute_reply":"2022-05-29T16:33:38.348046Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"check_model(LDA_model,X_train,y_train,X_test,'LDA model')","metadata":{"execution":{"iopub.status.busy":"2022-05-29T16:33:38.350599Z","iopub.execute_input":"2022-05-29T16:33:38.350839Z","iopub.status.idle":"2022-05-29T16:33:38.748970Z","shell.execute_reply.started":"2022-05-29T16:33:38.350810Z","shell.execute_reply":"2022-05-29T16:33:38.748054Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":" We can see the accuracy and validation scores did not improve by much.\n I decided not to use only the selected features, because the final score of the submission was lower this way.","metadata":{}},{"cell_type":"markdown","source":"Finding the best hyper parameters ","metadata":{}},{"cell_type":"code","source":"# train with grid search and get best parameters\nfrom sklearn.model_selection import GridSearchCV\nfrom sklearn.linear_model import SGDClassifier\n\nhyper_parameters = {\"solver\" : [\"svd\"],\n \"tol\" : [0.0001,0.0002,0.0003]}\ngs_model = GridSearchCV(LDA_model,param_grid = hyper_parameters, cv=5, scoring=\"accuracy\", n_jobs= 4, verbose = 1).fit(x_train_all,y_train_all)\nprint('Accuracy score for classification:')\nprint('gs_model', gs_model.best_score_)\nprint('best params', gs_model.best_params_)","metadata":{"execution":{"iopub.status.busy":"2022-05-29T16:33:38.750178Z","iopub.execute_input":"2022-05-29T16:33:38.750445Z","iopub.status.idle":"2022-05-29T16:33:40.379970Z","shell.execute_reply.started":"2022-05-29T16:33:38.750413Z","shell.execute_reply":"2022-05-29T16:33:40.379114Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Predict results ","metadata":{}},{"cell_type":"code","source":"predict_test = gs_model.predict(x_test_final)","metadata":{"execution":{"iopub.status.busy":"2022-05-29T16:33:40.381337Z","iopub.execute_input":"2022-05-29T16:33:40.381623Z","iopub.status.idle":"2022-05-29T16:33:40.390638Z","shell.execute_reply.started":"2022-05-29T16:33:40.381577Z","shell.execute_reply":"2022-05-29T16:33:40.389682Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"result_csv = pd.DataFrame({\"PassengerId\": data_test_passenger_id.values,\"Survived\": predict_test})\nresult_csv.to_csv(\"result_csv_exercise_3.csv\", index=False)","metadata":{"execution":{"iopub.status.busy":"2022-05-29T16:33:40.392330Z","iopub.execute_input":"2022-05-29T16:33:40.392988Z","iopub.status.idle":"2022-05-29T16:33:40.408497Z","shell.execute_reply.started":"2022-05-29T16:33:40.392942Z","shell.execute_reply":"2022-05-29T16:33:40.407863Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Summary: \nBy comparing and tuning the chosen model, I succeeded to get a good result. \nUnfortunately, the new score is not different from my previous score. It might have been better, if it was allowed to use other different models for this exercise. \n \n","metadata":{}},{"cell_type":"markdown","source":"![צילום מסך 2022-05-29 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מסך 2022-05-29 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\nhttps://towardsdatascience.com/kaggle-titanic-machine-learning-model-top-7-fa4523b7c40 \nhttps://www.kaggle.com/code/alqwizz89/implicit-data-in-titanic-top-3?scriptVersionId=61344798 \nhttps://www.kaggle.com/code/startupsci/titanic-data-science-solutions/notebook \nhttps://www.kaggle.com/code/vinothan/titanic-model-with-90-accuracy/notebook ","metadata":{}}]}