panicmilica,Milica Panic,github

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Name: Milica Panic

Type: User

Company: Faculty of Science, University of Novi Sad

Bio: BSc in Applied Mathematics. Soon MSc in Data Science. I love working on projects that include Python, SQL, ML, AI, smart agriculture and of course, math. :)

Location: Novi Sad, Serbia

👋 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.

Milica Panic's Projects

120-years-od-olympic-games-exploratory-analysis-python

Exploratory Data Analysis of Olympic games, using the Seaborn library. The first step was to clean the data, then to examine it and finally to visualize the data in order to make the necessary conclusions.

car-price-prediction-python

The goal of the project was to predict the price based on the given attributes of the car. It was done in Python, using Machine Learning techniques like Simple Linear Regression, Multiple Linear Regression and Decision tree.

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.