panicmilica/crop-classification

In this project, I built 140 models using Random Forest, Naive Bayes, KNN, Catboost, Dense, CNN and MLP classifier. I used pandas, numpy and tensorflow. The goal was to predict the classes of crop based on satellite images. This was also a kaggle competition where I won first place.

• 👋 Hi, I’m @PanicMilica
• 👀 I’m interested in Projects in Python.
• 🌱 I’m currently learning Machine Learning Algorithms.
• 💞️ I’m looking to collaborate on new projects based on AI, ML and DL.
• 📫 How to reach me [email protected]

For prediction of car price I used Simple Linear Regression, Multiple Linear Regression and Decision tree. There were two main goals that I wanted to do in this project. Firstly, I wanted to estimate the price of cars by taking into account a set of features, based on historical data. Secondly and most importantly is that by developing models I wanted to accurately predict car price based on its features, in order to make informed purchases. I implemented and evaluated various methods on a dataset consisting of prices of different makes and models. Results show that Multiple Linear Regression using Ordinary Least Squares (OLS) method gives really good results.