Here are my projects related to Data Analysis and Machine Learning.
In this binary classification task, One should implement a model to predict whether a person has a bank account or not given some information about the person.
Platform: Zindi
Type of Data: Tabular Data
Position: 1 out of 38 contestants
MAE: 0.109555087371421
Contest URL: https://zindi.africa/hackathons/indabax-sudan-2021
In this binary classification task, One should implement a model to predict whether a person has survived from the sinking of the Titanic or not given some information about the person.
Platform: Kaggle
Type of Data: Tabular Data
Accuracy Score: 78.4%
Contest URL: https://www.kaggle.com/c/titanic/overview
In this regression task, One should implement a model to predict a measure of wealth for different locations across Africa.
Platform: Zindi
Type of Data: Tabular Data
Position: 2 out of 107 contestants
RMSE: 0.1039661172059555
Contest URL: https://zindi.africa/competitions/umojahack-2022-practice-challenge
In this binary classification task, One should implement a model to predict whether a device is faulty based on its output data.
Platform: Zindi
Type of Data: Time Series Data
[Original Solution] Accuracy Score: 83.4%
[Updated Solution] Accuracy Score: 94.7%
[Original Solution] Position: 46 out of 719 contestants
[Updated Solution] Position: 1 out of 719 contestants
Contest URL: https://zindi.africa/competitions/umojahack-africa-2022-beginner-challenge
In this regression task, One should implement a model to predict what a tourist will spend when visiting Tanzania.
Platform: Zindi
Type of Data: Tabular Data
Position: 13 out of 308 contestants
MAE: 4971847.829238593
Contest URL: https://zindi.africa/competitions/tanzania-tourism-prediction
In this regression task, One should implement a model to forecast the store sales for the next six weeks of the data.
Platform: Kaggle
Type of Data: Time Series Data
RMSPE Score: 0.11323
Contest URL: https://www.kaggle.com/competitions/rossmann-store-sales/
In this multiclass classification task, One should implement a model to predict the outcome of a football match, based on historical match and player data.
Platform: Zindi
Type of Data: Time Series Data
Accuracy Score: 47.8%
Position: 4 out of 251 contestants
Contest URL: https://zindi.africa/competitions/zindi-weekendz-laduma-analytics-football-league-winners-prediction-challenge
In this regression task, One should implement a model to predict air quality in regions in Kampala, based on satellite radar data from Sentinel 5P.
Platform: Zindi
Type of Data: Time Series Data
MAE Score: 13.33173593948973
Position: 3 out of 153 contestants
Contest URL: https://zindi.africa/competitions/zindi-weekendz-layerai-air-quality-prediction-challenge/
In this multiclass classification task, One should implement a model to classify the range of expenditures a tourist spends in Tanzania.
Platform: Zindi
Type of Data: Tabular Data
Position: 32 out of 461 contestants
Log-Loss: 1.040574812022115
Contest URL: https://zindi.africa/competitions/ai4d-lab-tanzania-tourism-classification-challenge/
In this multiclass classification task, One should implement a model to classify purchases into 13 different categories, based on transactions data from Alvin.
Platform: Zindi
Type of Data: Tabular Data
Logloss Score: 1.372343425651699
Position: 8 out of 448 contestants
Contest URL: https://zindi.africa/competitions/alvin-smart-money-management-classification-challenge/
In this binary classification task, One should implement a model to predict the probability that a customer does not pay back their credit card balance amount in the future based on their monthly customer profile.
Platform: Kaggle
Type of Data: Tabular Data
AMEX Score: 0.80712
Position: 450 out of 4875 contestants (A Bronze Medal)
Contest URL: https://www.kaggle.com/competitions/amex-default-prediction/
In this regression task, One should implement a model to determine as accurately as possible the demand for each individual product, four months into the future.
Platform: Zindi
Type of Data: Time Series Data
MAE Score: 165985.6352266829
Position: 24 out of 827 contestants
Contest URL: https://zindi.africa/competitions/fossil-stock-forecasting-challenge/
In this classification task, One should implement a model to detect and recognize clients involved in fraudulent activities.
Platform: Zindi
Type of Data: Tabular Data
AUC Score: 0.8966153
Position: 1 out of 51 contestants
Contest URL: https://zindi.africa/competitions/indabax-sudan-classification2022/