wehub-resource-sync
55 lines
1.8 KiB
Python
55 lines
1.8 KiB
Python
import os
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import joblib
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import pandas as pd
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from sklearn.linear_model import LogisticRegression
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# --- Define file paths ---
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csv_file = "data/sample.csv"
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model_file = "model/model.pkl"
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# --- Pre-run Check for Data File ---
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# Check if the required CSV file exists before proceeding.
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if not os.path.exists(csv_file):
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print(f"Error: Data file '{csv_file}' not found.")
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# For this to work, ensure you have a 'data' directory with 'sample.csv' inside.
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exit()
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# --- Data Loading ---
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# Load the dataset from the specified CSV file.
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print(f"Loading data from '{csv_file}'...")
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df = pd.read_csv(csv_file)
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# --- Data Preparation ---
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# Separate the features (input variables) from the target (output variable).
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# X contains the features used for prediction.
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# y contains the target variable.
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print("Preparing data...")
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X = df[["feature1", "feature2"]]
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y = df["target"]
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# --- Model Training ---
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# Initialize a simple Logistic Regression model.
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# Then, train the model using our dataset (X and y).
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print("Training the model...")
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model = LogisticRegression()
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model.fit(X, y)
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# --- Check for Model ---
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# Get the model folder path from the model file.
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model_dir = os.path.dirname(model_file)
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# Create the model folder if it does not exist.
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# The 'exist_ok=True' argument prevents an error if it already exists.
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if not os.path.exists(model_dir):
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print(f"Directory '{model_dir}' not found. Creating it...")
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os.makedirs(model_dir, exist_ok=True)
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# --- Model Saving ---
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# Serialize the trained model object and save it to a file.
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# This allows you to load and use the model later without retraining.
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print(f"Saving the trained model to '{model_file}'...")
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joblib.dump(model, model_file)
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print("\nTraining complete!")
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print(f"The model has been saved as '{model_file}'.")
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