This project builds a predictive model for breast cancer detection using Random Forest. It includes Exploratory Data Analysis (EDA) to explore patterns, class distribution, correlations, outliers, and PCA. After preprocessing, a model is trained to classify tumors as malignant or benign, aiding early diagnosis.
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This project builds a predictive model for breast cancer detection using Random Forest. It includes Exploratory Data Analysis (EDA) to explore patterns, class distribution, correlations, outliers, and PCA. After preprocessing, a model is trained to classify tumors as malignant or benign, aiding early diagnosis.
mbabar1100/BreastCancerDetectionUsingRandomForest
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This project builds a predictive model for breast cancer detection using Random Forest. It includes Exploratory Data Analysis (EDA) to explore patterns, class distribution, correlations, outliers, and PCA. After preprocessing, a model is trained to classify tumors as malignant or benign, aiding early diagnosis.
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