Thanks to visit codestin.com
Credit goes to github.com

Skip to content
View NicholusMuthomi's full-sized avatar
💭
💡
💭
💡

Block or report NicholusMuthomi

Block user

Prevent this user from interacting with your repositories and sending you notifications. Learn more about blocking users.

You must be logged in to block users.

Maximum 250 characters. Please don’t include any personal information such as legal names or email addresses. Markdown is supported. This note will only be visible to you.
Report abuse

Contact GitHub support about this user’s behavior. Learn more about reporting abuse.

Report abuse

Popular repositories Loading

  1. Online-Retailer-in-UK Online-Retailer-in-UK Public

    Conducted RFM and market basket analysis on 500K+ transactions to segment customers and recommend products. Identified segments driving 80% of revenue and uncovered product affinities for cross-sel…

    Jupyter Notebook 6

  2. Fifa-2022 Fifa-2022 Public

    Analysed data on 16k+ players to identify performance patterns. Built similarity algorithms using K-Means and hierarchical clustering to support scouting and team selection.

    Jupyter Notebook 3

  3. Telco-Customer-Churn Telco-Customer-Churn Public

    Developed a machine learning model that can forecast telecom customer attrition, allowing for proactive retention tactics to lower attrition and boost income.

    Jupyter Notebook 2

  4. E-commerce-Sales-Analysis E-commerce-Sales-Analysis Public

    Used K-means clustering to analyse e-commerce customers by spending, returns and behavior. Identified high-value customers to improve targeted marketing and boost retention and enhancing overall sa…

    Jupyter Notebook 2

  5. Recommendation-System Recommendation-System Public

    Built a collaborative filtering model to predict user movie preferences from historical ratings. Analysed user similarity and item similarity to generate personalised recommendations. Deployed as a…

    Jupyter Notebook 2

  6. Spotify-Dataset-Analysis Spotify-Dataset-Analysis Public

    Analysed 20k+ tracks to identify audio features driving popularity. Found energy, danceability and loudness key for viral success. Compared Spotify/YouTube to guide release strategy.

    2