Regression Problem under Supervised Machine Learning using RMSE for model evaluation. Training data consists of 16k observations and four location variables, number of passengers and fare_amount (target variable) with missing values. Exploratory Data includes missing value analysis, outlier analysis and visualization of predictor variables with target variables. Feature engineering includes feature addition and selection. Finally using Light GBM to reach RMSE of $3.92.
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Data Science Project- Regression Problem under Supervised Machine Learning using RMSE for model evaluation. Training data consists of 55M observations and four location variables, number of passengers and fare_amount (target variable) with missing values. Exploratory Data includes missing value analysis, outlier analysis and visualization of pre…
KUSHAGRARAJTIWARI/NYC-Cab-Fare-Prediction
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Data Science Project- Regression Problem under Supervised Machine Learning using RMSE for model evaluation. Training data consists of 55M observations and four location variables, number of passengers and fare_amount (target variable) with missing values. Exploratory Data includes missing value analysis, outlier analysis and visualization of pre…
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