In this project, we have done an Exploratory Data Analysis of Boston Uber Data and End-to-End Predictive Analysis for Uber Price Prediction using Machine Learning.
Exploratory Data Analysis (EDA) and predictive analysis are crucial in understanding and utilizing data effectively. Here’s a breakdown of what each part of your project might involve:
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Data Collection: Gathering the Boston Uber data, which likely includes information such as ride timestamps, locations, prices, and possibly other factors like weather or traffic conditions.
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Data Cleaning: Preparing the data for analysis by handling missing values, and outliers, and ensuring data types are appropriate for analysis.
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Exploratory Analysis:
--> Summary Statistics: Calculating descriptive statistics (mean, median, standard deviation, etc.) to understand the distribution of variables.
--> Data Visualization: Using charts (histograms, box plots, scatter plots, etc.) to visualize relationships between variables and identify patterns or trends.
- Feature Engineering: Creating new features from existing data that might enhance the predictive power of your model. This could include deriving features like time of day, day of week, or distance between locations.
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Problem Formulation: Defining the prediction task clearly — in this case, predicting Uber prices based on various input features.
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Feature Selection: Choosing relevant features from your EDA phase that are likely to impact Uber prices. This might include factors like distance traveled, time of day, weather conditions, etc.
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Model Selection: Select appropriate machine learning models for regression (since you're predicting prices). This could range from simpler models like linear regression to more complex ones like decision trees, random forests, or even neural networks depending on the complexity of your data and prediction task.
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Model Training and Evaluation:
--> Training: Splitting your data into training and testing sets to train the model on one and evaluate its performance on the other.
--> Evaluation: Using metrics such as Mean Absolute Error (MAE), Mean Squared Error (MSE), or Root Mean Squared Error (RMSE) to evaluate how well your model predicts Uber prices.
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Model Deployment: Implementing the trained model into a usable format, potentially as part of an application or service.
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Iterative Improvement: Refining your model based on evaluation results, possibly trying different algorithms or tuning hyperparameters to improve predictive accuracy.
This project involves a comprehensive journey from understanding the dataset through EDA to building a predictive model for Uber price prediction. Each step is crucial for gaining insights from data and leveraging machine learning techniques to make predictions. Feel free to ask if you have more specific questions or need further guidance!