During this summer internship, I had the opportunity to work on the AMES House Prediction Model project and gain valuable experience in the field of data science. Throughout the internship, I learned and acquired skills in various aspects of the data science lifecycle, including data collection, preprocessing, exploratory data analysis, feature engineering, model selection, training, evaluation, and deployment.
One of the key aspects I learned during this internship was the importance of data preprocessing. I gained hands-on experience in handling missing values, dealing with categorical variables.
Exploratory Data Analysis (EDA) was another crucial area I delved into. By conducting statistical analysis, data visualization, and correlation analysis, I learned how to extract meaningful insights and discover patterns within the dataset. This process helped me understand the relationships between different features and the target variable, which, in this case, was the house price.
Feature engineering was an essential step that I explored to improve the model’s performance. Through techniques like feature scaling, one-hot encoding, and creating interaction terms, I learned how to enhance the predictive power of the dataset and derive additional relevant features.
Model selection played a vital role in this internship. I had the opportunity to explore and evaluate various machine learning algorithms such as linear regression, random forests. By comparing their performance using appropriate evaluation metrics, I learned to choose the most suitable model for predicting house prices accurately.
The training and evaluation phase helped me understand the process of splitting the dataset into training and testing sets, training the model, and assessing its performance. I learned to utilize evaluation metrics like mean squared error (MSE) and R-squared to measure the model’s accuracy and generalization capabilities.
Moreover, during the internship, I had the opportunity to learn and work with Git and GitHub. Git is a version control system that allows for efficient collaboration and tracking changes in code and project files. GitHub, on the other hand, is a web-based platform for hosting Git repositories and facilitating collaboration among team members. I learned how to create branches, commit changes, and merge code using Git and how to utilize GitHub to contribute to the project repository and track the project’s progress.
Overall, this internship provided me with invaluable experience in data science, including hands-on application of data analytics techniques, model development, and utilization of Git and GitHub for efficient project management and collaboration. It was a rewarding experience that further enhanced my skills and knowledge in the field of data science.
You can view my report on the same by visiting the link :- https://kaustubhnair26.github.io/OrionSummerInternship-2023/notebooks/report.html
Python 3.9.12
Quarto 1.3.353
requirement.txt
The AEMS House Prediction Model is a data science project aimed at developing a predictive model for house prices based on data collected from the Advanced Estate Management System (AEMS) database. The model utilizes various features such as house size, location, number of bedrooms, and other relevant attributes to estimate the price of a house. By leveraging machine learning algorithms and data analysis techniques, the model aims to provide accurate predictions to assist homeowners, real estate agents, and potential buyers in making informed decisions.
Welcometo the readme file for the AEMS House Prediction Model Summer Internship. This internship is focused on developing a data science model that predicts house prices using various features and data collected from the AEMS (Advanced Estate Management System) database. In this document, I will provide an overview of the prerequisites for this project and explain each step followed in detail.
To successfully complete this internship, the following prerequisites are required:
The first step involved collecting data from the AEMS database. The dataset consists of various attributes such as house size, location, number of bedrooms, and other relevant features.
In this step, I performed data preprocessing tasks such as handling missing values, handling categorical variables, and removing any outliers. Data preprocessing is essential to ensure the quality and reliability of the dataset.
EDA involves analyzing the dataset to gain insights and discover patterns. I performed statistical analysis, data visualization, and correlation analysis to understand the relationships between different features and the target variable (house price).
Feature engineering is the process of creating new features or transforming existing features to improve model performance. I applied techniques such as feature scaling, one-hot encoding, and creating interaction terms to enhance the predictive power of the dataset.
In this step, I explored various machine learning algorithms and evaluated their performance using appropriate evaluation metrics. I considered models such as linear regression, decision trees, random forests, and gradient boosting.
Once the model was selected, I split the dataset into training and testing sets. I trained the model on the training set and evaluated its performance on the testing set using metrics such as mean squared error (MSE) and R-squared.
During this summer internship, I had the opportunity to work on a real-world data science project and gain hands-on experience in various aspects of the data science lifecycle. It was a challenging yet rewarding experience that allowed me to apply my knowledge and skills in Python programming, data manipulation, exploratory data analysis, and machine learning.
Throughout the internship, I learned the importance of data preprocessing and feature engineering in improving model performance. I also gained insights into different machine learning algorithms and their strengths and weaknesses.
Working under a mentor provided me with valuable guidance and feedback. It was a great opportunity to enhance my collaboration and communication skills, as well as learn from industry professional.
Overall, this summer internship was an enriching experience that deepened my understanding of data science and its application in solving real-world problems.