Delete app.py

app.py

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-import pandas as pd
-import seaborn as sns
-import matplotlib.pyplot as plt
-from sklearn.model_selection import train_test_split
-import mlflow
-import mlflow.sklearn
-from sklearn.linear_model import LinearRegression
-from sklearn.tree import DecisionTreeRegressor
-from sklearn.ensemble import RandomForestRegressor, GradientBoostingRegressor
-from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score
-
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-from sklearn.linear_model import LinearRegression
-from sklearn.tree import DecisionTreeRegressor
-from sklearn.ensemble import RandomForestRegressor, GradientBoostingRegressor
-from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score
-from sklearn.model_selection import GridSearchCV
-import mlflow
-import mlflow.sklearn
-import joblib
-
-# Load dataset
-df = pd.read_csv('ds_salaries.csv')
-
-# EDA
-print(df.head())
-print(df.info())
-print(df.describe())
-
-# Visualizations
-sns.pairplot(df)
-plt.show()
-
-# Handle missing values
-# Fill numeric columns with mean and categorical columns with mode
-numeric_cols = df.select_dtypes(include=['float64', 'int64']).columns
-categorical_cols = df.select_dtypes(include=['object']).columns
-
-df[numeric_cols] = df[numeric_cols].apply(lambda x: x.fillna(x.mean()))
-df[categorical_cols] = df[categorical_cols].apply(lambda x: x.fillna(x.mode()[0]))
-
-# Drop the salary_currency column as it's not needed for prediction
-df = df.drop(columns=['salary_currency'])
-
-# Encode categorical variables
-categorical_columns = ['experience_level', 'employment_type', 'job_title', 'employee_residence', 'company_location', 'company_size']
-df = pd.get_dummies(df, columns=categorical_columns, drop_first=True)
-
-# Define features and target variable
-X = df.drop(['salary', 'salary_in_usd'], axis=1)
-y = df['salary_in_usd']
-
-# Split the data
-X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
-
-# Model training and experiment tracking with MLflow
-models = {
-    'Linear Regression': LinearRegression(),
-    'Decision Tree': DecisionTreeRegressor(),
-    'Random Forest': RandomForestRegressor(),
-    'Gradient Boosting': GradientBoostingRegressor()
-}
-
-mlflow.set_experiment('Data Science Salaries Prediction')
-
-for model_name, model in models.items():
-    with mlflow.start_run(run_name=model_name):
-        model.fit(X_train, y_train)
-        y_pred = model.predict(X_test)
-        
-        # Log model
-        mlflow.sklearn.log_model(model, model_name)
-        
-        # Log metrics
-        mlflow.log_metric('RMSE', mean_squared_error(y_test, y_pred, squared=False))
-        mlflow.log_metric('MAE', mean_absolute_error(y_test, y_pred))
-        mlflow.log_metric('R2', r2_score(y_test, y_pred))
-
-# Hyperparameter tuning for the best model (e.g., Random Forest)
-from sklearn.model_selection import GridSearchCV
-
-param_grid = {
-    'n_estimators': [100, 200, 300],
-    'max_depth': [None, 10, 20, 30]
-}
-
-grid_search = GridSearchCV(RandomForestRegressor(), param_grid, cv=3, scoring='r2')
-grid_search.fit(X_train, y_train)
-
-# Log the best model and parameters
-with mlflow.start_run(run_name='Optimized Random Forest'):
-    mlflow.sklearn.log_model(grid_search.best_estimator_, 'Random Forest')
-    mlflow.log_params(grid_search.best_params_)
-    mlflow.log_metric('Best R2', grid_search.best_score_)
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-# Model training and experiment tracking with MLflow
-models = {
-    'Linear Regression': LinearRegression(),
-    'Decision Tree': DecisionTreeRegressor(),
-    'Random Forest': RandomForestRegressor(),
-    'Gradient Boosting': GradientBoostingRegressor()
-}
-
-mlflow.set_experiment('Data Science Salaries Prediction')
-
-for model_name, model in models.items():
-    with mlflow.start_run(run_name=model_name):
-        model.fit(X_train, y_train)
-        y_pred = model.predict(X_test)
-        
-        # Log model
-        mlflow.sklearn.log_model(model, model_name)
-        
-        # Log metrics
-        mlflow.log_metric('RMSE', mean_squared_error(y_test, y_pred, squared=False))
-        mlflow.log_metric('MAE', mean_absolute_error(y_test, y_pred))
-        mlflow.log_metric('R2', r2_score(y_test, y_pred))
-
-# Hyperparameter tuning for the best model (e.g., Random Forest)
-param_grid = {
-    'n_estimators': [100, 200, 300],
-    'max_depth': [None, 10, 20, 30]
-}
-
-grid_search = GridSearchCV(RandomForestRegressor(), param_grid, cv=3, scoring='r2')
-grid_search.fit(X_train, y_train)
-
-# Log the best model and parameters
-with mlflow.start_run(run_name='Optimized Random Forest'):
-    mlflow.sklearn.log_model(grid_search.best_estimator_, 'Random Forest')
-    mlflow.log_params(grid_search.best_params_)
-    mlflow.log_metric('Best R2', grid_search.best_score_)
-
-# Save the best model
-joblib.dump(grid_search.best_estimator_, 'best_model.pkl')
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-import streamlit as st
-import pandas as pd
-import joblib
-
-# Load the original dataset to get unique values for dropdowns
-df_original = pd.read_csv('ds_salaries.csv')
-
-# Load the best model
-best_model = joblib.load('best_model.pkl')
-
-# Streamlit app
-st.title('Data Science Salary Predictor')
-
-# Input features
-experience_level = st.selectbox('Experience Level', df_original['experience_level'].unique())
-employment_type = st.selectbox('Employment Type', df_original['employment_type'].unique())
-job_title = st.selectbox('Job Title', df_original['job_title'].unique())
-employee_residence = st.selectbox('Employee Residence', df_original['employee_residence'].unique())
-remote_ratio = st.selectbox('Remote Ratio', df_original['remote_ratio'].unique())
-company_location = st.selectbox('Company Location', df_original['company_location'].unique())
-company_size = st.selectbox('Company Size', df_original['company_size'].unique())
-
-# Predict salary
-input_data = pd.DataFrame({
-    'work_year': [2023],
-    'experience_level': [experience_level],
-    'employment_type': [employment_type],
-    'job_title': [job_title],
-    'employee_residence': [employee_residence],
-    'remote_ratio': [remote_ratio],
-    'company_location': [company_location],
-    'company_size': [company_size]
-})
-
-# Encode categorical variables
-categorical_columns = ['experience_level', 'employment_type', 'job_title', 'employee_residence', 'company_location', 'company_size']
-input_data = pd.get_dummies(input_data, columns=categorical_columns, drop_first=True)
-
-# Align input data with training data columns
-input_data = input_data.reindex(columns=X_train.columns, fill_value=0)
-
-# Predict the salary
-salary_prediction = best_model.predict(input_data)[0]
-st.write(f'Predicted Salary: ${salary_prediction:.2f}')
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