codegotllama.txt

# Eda-regression Makefile

# Define variables
DATA_FILE = eda-regression.csv
OUTPUT_DIR = output/
MODEL_OUTPUT = model_output/

# Step 1: Import data
import-data:
    aws s3 cp s3://your-bucket-name/$DATA_FILE $OUTPUT_DIR
    gzip -d $OUTPUT_DIR$DATA_FILE.gz

# Step 2: Clean data (e.g. handle missing values, convert datatypes)
clean-data: import-data
    python clean_data.py $OUTPUT_DIR$DATA_FILE

# Step 3: Explore data using EDA tools (e.g. correlation matrix, histograms)
eda:
    python eda.py $OUTPUT_DIR$DATA_FILE

# Step 4: Split data into training and testing sets
split-data: clean-data eda
    python split_data.py $OUTPUT_DIR$DATA_FILE 0.8

# Step 5: Train a machine learning model (e.g. linear regression)
train-model: split-data
    python train_model.py $OUTPUT_DIR$DATA_FILE.split 0.2

# Step 6: Evaluate the trained model using metrics (e.g. mean squared error)
evaluate-model: train-model
    python evaluate_model.py $OUTPUT_DIR$MODEL_OUTPUT/model.pkl

# Default target (runs all steps)
default: import-data clean-data eda split-data train-model evaluate-model