Please write a study log on the 8 steps of the End-to-End Machine Learning Project. ? Look at the big picture. ? Get the data. ? Discover and visualize the data to gain insights. ? Prepare the data for Machine Learning algorithms. ? Select a model and train it. ? Fine-tune your model. ? Present your solution. ? Launch, monitor, and maintain your system. To begin, list what you have learned in each step and highlight the important techniques that can improve the training. Then, try to improve the example of housing price estimation. The improvements include but are not restricted to the following: ? Try other attribute combinations. ? Try other models. You need to provide justification of intuition on the proposed new features and models. The justification of statistics and figures is recommended but not mandatory. (No more than 1000 words)
Study Log:
Step 1: Look at the big picture In this step, the goal of the project is defined and the metrics to evaluate the success of the project are established. It is important to define the problem and understand how the solution will be used. For example, in the housing price estimation project, the goal is to predict the median housing price in a district based on a set of features. The success of the project can be measured by the accuracy of the predictions. The performance metric can be the Root Mean Square Error (RMSE), which measures the difference between the predicted and actual values.
Step 2: Get the data In this step, the data is obtained from various sources, such as databases or APIs. It is important to ensure that the data is representative of the problem and that it is large enough to be statistically significant. In the housing price estimation project, the data can be obtained from a public dataset such as the California Housing Prices dataset.
Step 3: Discover and visualize the data to gain insights In this step, the data is explored to identify patterns and correlations between the features. This can be done through visualization tools such as histograms, scatter plots, and correlation matrices. It is important to understand the distribution of the data, the presence of outliers, and any missing values. In the housing price estimation project, we can use tools such as Matplotlib and Seaborn to visualize the relationship between the features and the target variable.
Step 4: Prepare the data for Machine Learning algorithms In this step, the data is cleaned, transformed, and scaled to be used in Machine Learning algorithms. This includes handling missing values, encoding categorical features, and scaling numerical features. It is important to split the data into training and testing sets to evaluate the performance of the models. In the housing price estimation project, we can use techniques such as One-Hot encoding, Feature Scaling, and Imputation to prepare the data.
Step 5: Select a model and train it In this step, a suitable Machine Learning algorithm is chosen based on the problem and the features of the data. The data is then trained using the chosen algorithm. It is important to evaluate the performance of the model on the training set to identify potential issues such as overfitting. In the housing price estimation project, we can use algorithms such as Linear Regression, Decision Trees, and Random Forests.
Step 6: Fine-tune your model In this step, the hyperparameters of the model are optimized to improve its performance. This can be done using techniques such as Grid Search, Randomized Search, or Bayesian Optimization. It is important to evaluate the performance of the model on the testing set to ensure that it is not overfitting. In the housing price estimation project, we can fine-tune the hyperparameters of the chosen algorithm to improve its performance.
Step 7: Present your solution In this step, the results of the model are presented to stakeholders in a clear and concise manner. This can include visualizations, tables, or reports. It is important to explain the limitations of the model and the assumptions made. In the housing price estimation project, we can present the results of the model as a set of predicted median housing prices for different districts.
Step 8: Launch, monitor, and maintain your system In this step, the model is deployed in a production environment and is monitored for its performance. It is important to update the model regularly to ensure that it remains accurate and relevant. In the housing price estimation project, we can deploy the model in a web application or a mobile app to make it accessible to users.
Improvements to the housing price estimation example:
Try other attribute combinations: We can try different combinations of features to see which combination gives the best results.
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