In the beginning, I clearly defined the purpose of my AI project. I wanted to build a model that could classify data into two different categories (0 or 1) based on two input values.This type of problem is common in many real-world situations — for example, predicting whether a student will pass or fail based on their test scores, or whether a customer will buy a product based on their spending habits.I decided that my input features would be two numerical values and that the output would be either 0 or 1.By setting this goal, I gave my AI project a clear direction and decided what data was needed to solve it.
After defining the problem, the next step was to gather data.In my project, I used a dataset named data_raw, which contains multiple rows of information.Each row has two numbers representing input features (Feature 1 and Feature 2) and a label (0 or 1) showing the result. If this were a real-world problem, the data could come from tests, sensors, surveys, or experiments.In my case, this dataset helps the AI learn the relationship between the two features and the label.Good quality data is essential because it directly affects how well the model can learn and make accurate predictions.
Once I had the data, I began to explore and understand it. I used Python and pandas to create a table showing all the rows and columns, and I calculated summary statistics like mean, minimum, and maximum values. I also used matplotlib to plot the data on a scatter graph, where each point represents one record from the dataset. In the graph, points labeled “0” and “1” were colored differently to easily see how they are spread out. This helped me identify patterns and relationships — for example, clusters or trends — which is important before training the AI model. Data exploration allows us to notice if there are any unusual values or if the two features are clearly separable.
After exploring the data, I moved to the modeling phase. This is where the AI actually learns from the data. If I were to continue this project, I would use a Machine Learning algorithm such as Logistic Regression or Support Vector Machine. These algorithms look at the input features and try to find the best mathematical relationship that separates label 0 from label 1. During training, the model adjusts its internal parameters to minimize error and improve prediction accuracy. The model’s goal is to generalize from the training data so it can make correct predictions when it sees new, unseen data.
Once the model is trained, it must be evaluated to see how well it performs. I would test the model using a portion of data that was not used for training. Then I could measure its performance using evaluation metrics such as accuracy, precision, recall, or F1-score. For example, if the model correctly predicts most of the labels, it has a high accuracy. If not, I might need to adjust the data, change the model, or tune its parameters. Evaluation helps determine whether the model is reliable enough to be used in real-life scenarios.