Week 1: Data collection, cleaning, and preprocessing.

[GNAR1ZARD]

• [x] Data Collection: Secure access to the cryptocurrency historical prices dataset, ensuring it contains all necessary features.
• [x] Data Inspection: Examine the dataset for missing values, outliers, or inconsistencies that could affect analysis.
• [x] Data Cleaning: Address any identified issues by removing, imputing, or correcting faulty data points.
• [x] Feature Selection: Determine which features are relevant to your prediction models and exclude irrelevant ones to streamline analysis.
• [x] Data Preprocessing: Normalize or standardize numerical data to ensure model compatibility and improve training efficiency. This step may also include encoding categorical variables if present.

[GNAR1ZARD]

Data Collection:

https://www.kaggle.com/datasets/sudalairajkumar/cryptocurrency-historical-prices-coingecko

[GNAR1ZARD]

Data Inspection:

• [x] Check for Missing Values
• [x] Check for Outliers
• [x] Check for Inconsistencies
• [x] Check for Duplicate Entries
• [x] Check Data Range

[dusek2]

• [x] There are no faulty data points. Cleaning done

Next step -> decide what features to pick.

[dusek2]

Features: Volume, Market Cap, Date