Update README.md

README.md

@@ -6,4 +6,4 @@ Thanks for checking out my portfolio. This repository contains my solo projects
 # Projects
 - **Railway Infrastructure and Economic Growth in the EU**: In [this](https://github.com/bamiro/bamiro.github.io/blob/main/Economic%20Growth%20and%20Public%20Transport%20in%20the%20EU.sqbpro) SQL project, I used data from eurostat to produce summary statistics on the level of railway and bus provision for 24 EU member states. I joined data from a macroeconomic dataset from the World Bank Organisation to provide further insights such as GDP growth, railway density, buses per capita and railway per capita. My results suggest a strong correlation between the number of buses per 1000 people and gdp per capita growth in the EU. Click [here](https://public.tableau.com/views/PublicTransportandEconomicGrowthintheEU/Dashboard2?:language=en-GB&:sid=&:redirect=auth&:display_count=n&:origin=viz_share_link) for Tableau visualisations, or [here](https://drive.google.com/drive/folders/1JO3KrwLJ06GzXwukxadTK6lTtLEC22UC?usp=share_link) to acces the data files used in this project.
 
-- **Predicting Yearly Customer Spending in E-commerce Using Linear Regression** In [this](url) Python project, I analyse ecommerce sales to predict the yearly amount spent by customers based on features such as average session length, time on app, time on website, and length of membership. After loading and exploring the data, a linear regression model is built to examine the relationship between these features and yearly spending. The data is split into training and testing sets, and the model is trained on the training data. The model's accuracy is evaluated using metrics like Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE), with residuals analyzed to assess model performance
+- **Predicting Yearly Customer Spending in E-commerce Using Linear Regression** In [this](https://github.com/bamiro/bamiro.github.io/blob/8a2e66268486cc9fef1ce72f4de51a47812f17cb/ecommerce) Python project, I analyse ecommerce sales to predict the yearly amount spent by customers based on features such as average session length, time on app, time on website, and length of membership. After loading and exploring the data, a linear regression model is built to examine the relationship between these features and yearly spending. The data is split into training and testing sets, and the model is trained on the training data. The model's accuracy is evaluated using metrics like Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE), with residuals analyzed to assess model performance