uploaded dataset and app.py file

uploaded dataset and app.py file as it wasn't accepting through terminal

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
+
+
+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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+
+
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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}')
+
+
+
+