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Project Report: Logistic Regression on MapReduce

Name: Neeraj Vishwanath
Roll No: SE21UARI095
Introduction:
This project aims to implement logistic regression using the MapReduce programming model to analyze and predict prices of Mercedes-Benz vehicles based on their mileage and rating. Logistic regression is a popular statistical method used for binary classification problems. In this project, we leverage the distributed processing capabilities of Hadoop MapReduce to handle large-scale data and perform logistic regression efficiently.

Dataset:
The input dataset is a CSV file named "usa_mercedes_benz_prices.csv" containing information about Mercedes-Benz vehicles. Each row in the dataset represents a vehicle and includes the following columns:
- Name: The name or model of the vehicle.
- Mileage: The mileage of the vehicle in miles.
- Rating: The rating of the vehicle.
- Review Count: The number of reviews for the vehicle.
- Price: The price of the vehicle in US dollars.

Implementation:
The logistic regression algorithm is implemented using Java and the Hadoop MapReduce framework. The implementation consists of three main components:

1. LogisticRegressionDriver:
   - This is the driver class responsible for configuring and running the MapReduce job.
   - It sets up the job configuration, specifies the mapper and reducer classes, and defines the input and output paths.
   - The driver class ensures the proper execution of the MapReduce job.

2. LogisticRegressionMapper:
   - The mapper class processes each line of the input dataset.
   - It extracts the relevant features (mileage and rating) and the target variable (price) from each line.
   - The mapper emits key-value pairs, where the key is the feature name ("mileage", "rating", or "price") and the value is the corresponding value.

3. LogisticRegressionReducer:
   - The reducer class receives the key-value pairs emitted by the mapper.
   - It collects the values for each feature and the target variable.
   - The reducer performs logistic regression training using gradient descent.
   - It initializes the coefficients randomly and iteratively updates them based on the predictions and errors.
   - The logistic regression model is trained for a specified number of iterations.
   - After training, the reducer calculates the sum of squared errors (SSE), total sum of squares (SST), and R-squared value to evaluate the model's performance.
   - Finally, the reducer emits the trained coefficients and the R-squared value as the output.

Execution:
To run the logistic regression MapReduce job, follow these steps:
1. Compile the Java source code files (LogisticRegressionDriver.java, LogisticRegressionMapper.java, LogisticRegressionReducer.java) into a JAR file.
2. Copy the input dataset file ("usa_mercedes_benz_prices.csv") to the Hadoop Distributed File System (HDFS).
3. Execute the MapReduce job using the Hadoop command-line interface, specifying the JAR file, the main class (LogisticRegressionDriver), and the input and output paths in HDFS.

Output:
The output of the logistic regression MapReduce job is stored in the specified output directory in HDFS. The output consists of the following key-value pairs:
- "Intercept": The intercept term of the logistic regression model.
- "Mileage Coefficient": The coefficient corresponding to the mileage feature.
- "Rating Coefficient": The coefficient corresponding to the rating feature.
- "R-squared": The R-squared value indicating the goodness of fit of the logistic regression model.

Results and Analysis:
The trained logistic regression model provides insights into the relationship between the input features (mileage and rating) and the target variable (price) of Mercedes-Benz vehicles. The coefficients indicate the impact of each feature on the price, while the R-squared value measures the proportion of variance in the price that can be explained by the features.

A higher R-squared value suggests a better fit of the model to the data. The coefficients can be interpreted as follows:
- Intercept: The base price of a Mercedes-Benz vehicle when mileage and rating are zero.
- Mileage Coefficient: The change in price for a unit increase in mileage, keeping the rating constant.
- Rating Coefficient: The change in price for a unit increase in rating, keeping the mileage constant.

The output of the logistic regression model can be used to make predictions on new, unseen data points by applying the trained coefficients to the input features.

Conclusion:
This project demonstrates the implementation of logistic regression using MapReduce on a dataset of Mercedes-Benz vehicle prices. By leveraging the distributed processing capabilities of Hadoop, the logistic regression algorithm can efficiently handle large-scale data and provide valuable insights into the relationship between vehicle features and prices.

The project showcases the power of combining statistical models like logistic regression with distributed computing frameworks like MapReduce to analyze and make predictions on complex datasets. The results obtained from the trained model can be utilized for pricing strategies, market analysis, and decision-making processes in the automotive industry.

Future enhancements to the project could include feature engineering, handling missing values, regularization techniques, and model evaluation using cross-validation. Additionally, the logistic regression model could be extended to handle multi-class classification problems by using techniques like one-vs-all or softmax regression.

Overall, this project serves as a foundation for applying logistic regression on MapReduce and can be adapted to various domains and datasets where binary classification problems are encountered.