The video is a tutorial on building a machine learning project to predict the number of medals a country will win in the Olympics based on data from previous Olympic games. The tutorial is divided into seven steps:
1. **Form a Hypothesis**: The project's hypothesis is that the number of medals a country will win in the Olympics can be predicted using data from previous games.
2. **Find and Prepare the Data**: The data used in this project comes from the summer Olympics from 1988 to 2016. The dataset includes information such as the team name, the year of the games, the number of athletes the team entered, the number of medals the country won in the previous games, and the number of medals the country won in the current games.
3. **Clean the Data**: The data is cleaned to handle missing values. In this case, the 'previous medals' column contains missing values for teams that did not participate in the previous Olympics. These rows are removed from the dataset.
4. **Choose an Error Metric**: The error metric used in this project is Mean Absolute Error (MAE). This metric measures the average of the absolute differences between the actual and predicted values.
5. **Split the Data**: The data is split into a training set and a test set. The training set is used to train the machine learning model, while the test set is used to evaluate the model's performance.
6. **Train the Model**: The project uses a linear regression model to make predictions. The model is trained using the 'athletes' and 'previous medals' columns as predictors and the 'medals' column as the target.
7. **Evaluate the Model**: The model's performance is evaluated using the MAE metric. The model's predictions are compared to the actual number of medals won by each country in the test set.
The tutorial provides a detailed walkthrough of each step, including code examples and explanations. It also discusses the importance of data cleaning, the choice of error metric, and the process of splitting the data. The tutorial concludes with an evaluation of the model's performance, highlighting the importance of considering the variability in the number of medals across different countries.
1. The tutorial begins by outlining a seven-step process for building a machine learning project. The goal is to predict how many medals a country will win in the Olympics using Python and Jupyter Notebook .
2. The first step involves forming a hypothesis, which is a statement that can be proven or disproved using data. For this project, the hypothesis is that the number of medals a country wins in the Olympics can be predicted using data .
3. The data used for this project is from the summer Olympics, and the dataset contains more than 2000 rows. Each row represents a single country in a single Olympic game .
4. The data set includes a team code, the year of the Olympic games, the number of athletes the country entered into the Olympics, the number of medals they won in the previous Olympics, and the number of medals they won in the current Olympics .
5. The data needs to be reshaped to make machine learning predictions. In this case, the data is already in the form needed .
6. Cleaning the data involves making sure that it is ready for machine learning. In this case, some data contains missing values, and these need to be handled .
7. The fourth step in the process involves cleaning the data to handle missing values. Most machine learning algorithms cannot work with missing data .
8. The fifth step is to find an error metric that can be used to evaluate the performance of the machine learning model. The error metric chosen is the mean absolute error .
9. The sixth step involves splitting the data into a training set and a test set. The training set is used to train the algorithm, and the test set is used to evaluate the performance of the algorithm .
10. The final step is to train the model. In this case, linear regression is used, a popular machine learning model .