Delete heartdisease.py

heartdisease.py

@@ -1,144 +0,0 @@
-# import the library
-import pandas as pd
-import numpy as np
-import seaborn as sns
-from sklearn.preprocessing import OrdinalEncoder, OneHotEncoder
-from sklearn.preprocessing import StandardScaler
-
-from sklearn.impute import KNNImputer
-from sklearn.pipeline import Pipeline
-from sklearn.compose import ColumnTransformer
-
-
-from sklearn.linear_model import LogisticRegression
-from sklearn.ensemble import RandomForestClassifier
-from sklearn.ensemble import GradientBoostingClassifier
-
-#libraries for model evaluation
-import matplotlib.pyplot as plt
-from sklearn.metrics import accuracy_score
-from sklearn.metrics import plot_confusion_matrix
-from sklearn.metrics import classification_report
-
-import warnings
-warnings.filterwarnings('ignore')
-
-# read the dataset
-df = pd.read_csv('heart.csv')
-
-# get categorical columns
-categorical_cols= df.select_dtypes(include=['object'])
-
-# get count of unique values for categorical columns
-for cols in categorical_cols.columns:
-    print(cols,':', len(categorical_cols[cols].unique()),'labels')
-
-# categorical columns
-cat_col = categorical_cols.columns
-
-# numerical column
-num_col = ['Age','RestingBP','Cholesterol','FastingBS','MaxHR','Oldpeak']
-
-# define X and y
-X = df.drop(['HeartDisease'],axis=1)
-y = df['HeartDisease']
-
-# create a pipeline for preprocessing the dataset
-
-num_pipeline = Pipeline([
-        ('imputer', KNNImputer(n_neighbors=5)),
-        ('std_scaler', StandardScaler()),
-    ])
-
-num_attribs = num_col 
-cat_attribs = cat_col
-
-# apply transformation to the numerical and categorical columns
-full_pipeline = ColumnTransformer([
-        ("num", num_pipeline, num_attribs),
-        ("cat", OneHotEncoder(), cat_attribs),
-    ])
-
-X = full_pipeline.fit_transform(X)
-
-# save preprocessed data
-temp_df = pd.DataFrame(X)
-temp_df.to_csv('processed_data.csv')
-
-# Splitting the dataset into the Training set and Test set
-from sklearn.model_selection import train_test_split
-X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 42)
-
-# count plot for number of heart disease(1)/No heart disease(0)
-import seaborn as sns
-sns.countplot(y_train,palette='OrRd')
-
-# create a fresh model based on tuned parameters
-rfc1=RandomForestClassifier(random_state=42, max_features='sqrt', n_estimators= 50, max_depth=7, criterion='gini')
-
-rfc1.fit(X_train, y_train)
-
-# Predicting the Test set results
-y_pred = rfc1.predict(X_test)
-print('Random forest accuracy_score:',accuracy_score(y_test,y_pred))
-
-# Save the Model
-
-import pickle
-
-# save the random forest model for future use
-pickle.dump(rfc1, open('rfc.pickle', 'wb'))
-
-# save the preprocessing pipeline
-pickle.dump(full_pipeline, open('full_pipeline.pickle', 'wb'))
-
-# Load the Models for future use
-
-rfc_saved = pickle.load(open('rfc.pickle','rb'))
-full_pipeline_saved = pickle.load(open('full_pipeline.pickle','rb'))
-
-# Visualization
-
-target = df['HeartDisease'].replace([0,1],['Low','High'])
-
-data = pd.crosstab(index=df['Sex'],
-           columns=target)
-
-data.plot(kind='bar',stacked=True)
-plt.show()
-
-plt.figure(figsize=(10,5))
-bins=[0,30,50,80]
-sns.countplot(x=pd.cut(df.Age,bins=bins),hue=target,color='r')
-plt.show()
-
-plt.figure(figsize=(10,5))
-sns.countplot(x=target,hue=df.ChestPainType)
-plt.xticks(np.arange(2), ['No', 'Yes']) 
-plt.show()
-
-plt.figure(figsize=(10,5))
-sns.countplot(x=target,hue=df.ExerciseAngina)
-plt.xticks(np.arange(2), ['No', 'Yes']) 
-plt.show()
-
-# feature importance
-
-# get important features used by model 
-importances = rfc1.feature_importances_
-feature_names = num_col
-for i in cat_col:
-    feature_names = feature_names + [i]*df[i].nunique()
-
-import pandas as pd
-
-forest_importances = pd.Series(importances, index=feature_names)
-
-forest_importances = forest_importances.groupby(level=0).first().sort_values(ascending=False)
-
-# plot the features based on their importance in model performance.
-fig, ax = plt.subplots()
-forest_importances.plot.bar()
-ax.set_title("Feature importances using MDI")
-ax.set_ylabel("Mean decrease in impurity")
-fig.tight_layout()