Title
Predicting Car price by Machine Learning
Introduction
In this portfolio project, I embarked on the journey of predicting car prices through the lens of machine learning. The objective was to harness the power of data science to create a robust model capable of estimating the price of a car based on various features. This project encompasses a comprehensive exploration of the entire data science pipeline, from data acquisition to model deployment.
Processing Steps
1) Data Reading: The project initiated with the crucial step of gathering relevant datasets containing information about various car features and their corresponding prices. This foundational phase laid the groundwork for subsequent exploration and analysis.
2) Data Cleaning: Rigorous data cleaning procedures were implemented to ensure the dataset's quality. This involved checking missing values, correcting inconsistencies, and addressing outliers – all crucial steps to create a clean and reliable dataset.
3) Exploratory Data Analysis (EDA): Employing EDA techniques, I delved into the dataset to gain deeper insights. Visualization tools and statistical analyses were used to identify patterns, correlations, and potential factors influencing car prices.
4) Analysing with Questions: A structured approach was taken to analyse the data by formulating specific questions. This step aimed to extract meaningful insights and answers from the dataset, contributing to a more informed understanding of the factors influencing car prices.
5) Data Pre-processing: To prepare the data for modelling, I engaged in feature transformation, handling categorical variables, and scaling numerical attributes. This step was crucial to optimize the dataset for consumption by machine learning algorithms.
6) Machine Learning Model: The core of the project involved the implementation and fine-tuning of a machine learning model. I explored various algorithms and trained the model on the pre-processed data to accurately predict car prices. The model's performance was rigorously assessed using appropriate evaluation metrics.
Output
Conclusion
In conclusion, this portfolio project showcases a holistic approach to predicting car prices using machine learning. From the initial stages of data gathering to the intricate steps of cleaning, exploration, and modelling, each phase played a crucial role in shaping the final predictive model. The project underscores the importance of a systematic and iterative process in achieving accurate predictions and offers valuable insights for future endeavours in data science and machine learning.
Source
GitHub: For all files
