PROJECT TITLE

Bitcoin Price Prediction

GOAL

Aim- To predict the price of the Bitcoin using Linear Regression, Lasso Regression & Decision Tree Regressor. Perform EDA

DATASET

https://www.kaggle.com/vikramjeetsinghs/bitcoin-dataset

DESCRIPTION

This is a regression problem where we need to predict the price of Bitcoin. We use Linear, Lasso Regression and Decision tree Regressor

WHAT I HAD DONE

1. perfromed exploratory data analysis (EDA) on the given dataset
2. it starts with loading the dataset and viewing the top 5 rows
3. we calculate statistical data in the dataset
4. then comes finding correlation between the features and also finding statistical values related to the dataset
5. data visualization is done with libraries such as matplotlib and seaborn
6. finally 3 different algorithms are used to find the best algorithm
7. also accuracy score of each algorithm is calculated for comparison purpose with other algorithms

MODELS USED

1. Linear Regression= simplest and most common algorithm used for classification problems
2. Ridge Regression
3. Support Vector Regressor

LIBRARIES NEEDED

1. Numpy
2. Pandas
3. Matplotlib
4. Seaborn
5. Scikit-Learn

ACCURACIES

1. Linear Regression Score = 0.9999539378804362
2. Ridge Regression Score = 0.9965513756246261
3. Support Vector Regressor Score = 0.13660060536688778

CONCLUSION

We can conclude that Linear Regression gives the most accurate results specifically for this problem statement.

CONTRIBUTED BY

Tandrima Singha