VinnoGS commited on
Commit
9252403
1 Parent(s): ef2a65b

Upload 5 files

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Files changed (5) hide show
  1. app.py +114 -0
  2. insurance.csv +1339 -0
  3. random_forest_pipeline.pkl +3 -0
  4. requirements.txt +7 -0
  5. train.py +88 -0
app.py ADDED
@@ -0,0 +1,114 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ # Import the libraries
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+ import joblib
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+ import pandas as pd
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+ import numpy as np
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+ import json
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+ import uuid
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+ from pathlib import Path
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+ import gradio as gr
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+ from sklearn.compose import make_column_transformer
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+ from sklearn.preprocessing import StandardScaler, OneHotEncoder
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+ from sklearn.impute import SimpleImputer
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+ from sklearn.pipeline import Pipeline
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+
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+
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+ # Run the training script placed in the same directory as app.py
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+ # The training script will train and persist a linear regression
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+ # model with the filename 'model.joblib'
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+ exec(open("train.py").read())
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+
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+ #from commit_scheduler import CommitScheduler
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+
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+ # Load the freshly trained model from disk
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+ saved_model = joblib.load("random_forest_pipeline.pkl")
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+ print("Model loaded from random_forest_pipeline.pkl")
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+
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+ # Prepare the logging functionality
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+ log_file = Path("logs/") / f"data_{uuid.uuid4()}.json"
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+ log_folder = log_file.parent
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+ log_folder.mkdir(parents=True, exist_ok=True)
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+
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+ # Simple lock mechanism for logging
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+ class SimpleLock:
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+ def __init__(self, lock_file="lockfile"):
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+ self.lock_file = Path(lock_file)
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+
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+ def __enter__(self):
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+ while self.lock_file.exists():
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+ pass # Simple spinlock
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+ self.lock_file.touch()
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+
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+ def __exit__(self, exc_type, exc_val, exc_tb):
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+ self.lock_file.unlink()
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+
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+ scheduler = SimpleLock()
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+
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+
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+ #scheduler = CommitScheduler(
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+ # repo_id="insurance-charge-mlops-logs", # provide a name "insurance-charge-mlops-logs" for the repo_id
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+ # repo_type="dataset",
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+ # folder_path=log_folder,
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+ # path_in_repo="data",
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+ # every=2
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+ #)
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+
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+ # Define the predict function which will take features, convert to dataframe and make predictions using the saved model
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+ # the functions runs when 'Submit' is clicked or when a API request is made
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+
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+ def predict(age, bmi, children, sex, smoker, region):
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+ # Create a DataFrame with the input data
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+ data = {
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+ 'age': [age],
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+ 'bmi': [bmi],
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+ 'children': [children],
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+ 'sex': [sex],
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+ 'smoker': [smoker],
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+ 'region': [region]
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+ }
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+ df = pd.DataFrame(data)
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+ # Make prediction using the loaded model
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+ prediction = saved_model.predict(df)
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+
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+
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+ # While the prediction is made, log both the inputs and outputs to a log file
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+ # While writing to the log file, ensure that the commit scheduler is locked to avoid parallel
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+ # access
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+
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+ # with scheduler.lock:
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+ with scheduler:
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+ with log_file.open("a") as f:
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+ f.write(json.dumps(
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+ {
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+ 'age': age,
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+ 'bmi': bmi,
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+ 'children': children,
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+ 'sex': sex,
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+ 'smoker': smoker,
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+ 'region': region,
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+ 'prediction': prediction[0]
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+ }
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+ ))
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+ f.write("\n")
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+
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+ return prediction[0]
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+
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+
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+
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+ # Set up UI components for input and output
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+ age_input = gr.Number(label="Age")
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+ bmi_input = gr.Number(label="BMI")
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+ children_input = gr.Number(label="Children")
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+ sex_input = gr.Radio(choices=['male', 'female'], label="Sex")
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+ smoker_input = gr.Radio(choices=['yes', 'no'], label="Smoker")
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+ region_input = gr.Dropdown(choices=['northeast', 'northwest', 'southeast', 'southwest'], label="Region")
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+
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+ # Create the gradio interface, make title "HealthyLife Insurance Charge Prediction"
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+ demo = gr.Interface(
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+ fn=predict,
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+ inputs=[age_input, bmi_input, children_input, sex_input, smoker_input, region_input],
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+ outputs="number",
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+ title="HealthyLife Insurance Charge Prediction"
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+ )
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+ # Launch with a load balancer
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+ demo.queue()
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+ demo.launch(share=False, show_error=True)
insurance.csv ADDED
@@ -0,0 +1,1339 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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random_forest_pipeline.pkl ADDED
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1
+ version https://git-lfs.github.com/spec/v1
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+ oid sha256:1fcd56e37864a9f27f239fbd6fc9f1a83428eb6989de3fcf2ac89a086795fef4
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+ size 2491568
requirements.txt ADDED
@@ -0,0 +1,7 @@
 
 
 
 
 
 
 
 
1
+ scikit-learn==1.3.0
2
+ joblib==1.2.0
3
+ numpy==1.24.3
4
+ scipy==1.11.1
5
+ threadpoolctl==2.2.0 # Installing correct version 3.1.0 if necessary
6
+ pandas==2.0.3
7
+ gradio==4.36.1
train.py ADDED
@@ -0,0 +1,88 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+
2
+ import joblib
3
+ import pandas as pd
4
+ import numpy as np
5
+ from sklearn.preprocessing import StandardScaler, OneHotEncoder
6
+ from sklearn.compose import make_column_transformer
7
+ from sklearn.impute import SimpleImputer
8
+ from sklearn.pipeline import Pipeline
9
+ from sklearn.model_selection import train_test_split
10
+ from sklearn.ensemble import RandomForestRegressor
11
+ from sklearn.metrics import mean_squared_error, r2_score, mean_absolute_error
12
+
13
+ # Load the dataset
14
+ df = pd.read_csv("insurance.csv")
15
+
16
+ # Define features and target
17
+ target = 'charges'
18
+ numerical_features = ['age', 'bmi', 'children']
19
+ categorical_features = ['sex', 'smoker', 'region']
20
+
21
+ print("Creating data subsets")
22
+
23
+ X = df[numerical_features + categorical_features]
24
+ y = df[target]
25
+
26
+ Xtrain, Xtest, ytrain, ytest = train_test_split(
27
+ X, y,
28
+ test_size=0.2,
29
+ random_state=42
30
+ )
31
+
32
+ # Define the numerical and categorical pipelines
33
+ numerical_pipeline = Pipeline([
34
+ ('imputer', SimpleImputer(strategy='median')),
35
+ ('scaler', StandardScaler())
36
+ ])
37
+
38
+ categorical_pipeline = Pipeline([
39
+ ('imputer', SimpleImputer(strategy='most_frequent')),
40
+ ('onehot', OneHotEncoder(handle_unknown='ignore'))
41
+ ])
42
+
43
+ preprocessor = make_column_transformer(
44
+ (numerical_pipeline, numerical_features),
45
+ (categorical_pipeline, categorical_features)
46
+ )
47
+
48
+ # Define the Random Forest model with the best parameters
49
+ model_random_forest = RandomForestRegressor(
50
+ n_estimators=125,
51
+ min_samples_split=3,
52
+ min_samples_leaf=4,
53
+ max_depth=25,
54
+ random_state=42,
55
+ n_jobs=-1
56
+ )
57
+
58
+ print("Estimating Best Model Pipeline")
59
+
60
+ model_pipeline = Pipeline([
61
+ ('preprocessor', preprocessor),
62
+ ('regressor', model_random_forest)
63
+ ])
64
+
65
+ # Train the model
66
+ model_pipeline.fit(Xtrain, ytrain)
67
+
68
+ # Predict on the test set
69
+ y_pred = model_pipeline.predict(Xtest)
70
+
71
+ # Calculate evaluation metrics
72
+ mae = mean_absolute_error(ytest, y_pred)
73
+ mse = mean_squared_error(ytest, y_pred)
74
+ rmse = np.sqrt(mse)
75
+ r2 = r2_score(ytest, y_pred)
76
+
77
+ print("Logging Metrics")
78
+ print(f"Mean Absolute Error (MAE): {mae}")
79
+ print(f"Mean Squared Error (MSE): {mse}")
80
+ print(f"Root Mean Squared Error (RMSE): {rmse}")
81
+ print(f"R-squared (R²): {r2}")
82
+
83
+ print("Serializing Model")
84
+
85
+ # Save the model to a file
86
+ saved_model_path = "random_forest_pipeline.pkl"
87
+ joblib.dump(model_pipeline, saved_model_path)
88
+ print(f"Model saved as {saved_model_path}")