Abstract : This project involves utilizing a cancer patient dataset to apply machine learning algorithms in predicting cancer. Machine learning offers powerful tools for identifying patterns within complex medical data, allowing for early and accurate cancer diagnoses. Key algorithms, such as Decision Trees, Support Vector Machines, K-Nearest Neighbors, and Neural Networks, are implemented to evaluate their effectiveness in distinguishing between cancerous and non-cancerous cases. By training these models on relevant features like tumor size, cell structure, and other biomarkers, the study aims to improve the accuracy and reliability of cancer prediction. The results from different models are compared to identify the most effective approach, helping medical professionals make more informed decisions and offering potential insights into automated cancer screening systems.

Pipeline of email classification problem

Note : If the code doesn't run, note down the paths to the files because I'm changing them.

Project construction

1. Read the file

2. Data Preprocessing

3. Data visualization

3.1. Word Clouds for Ham and Spam Messages

3.2. Count occurrences of ham and spam

3.3. The chart shows the number of words in the message

4. Train-Test Split

5. Convert text to numbers

6. Building a Naive Bayes Model

7. Model Evaluation

7.1. Confusion Matrix

8. Predictions on new data

With label 1.0 being spam and label 0.0 being ham

Email: "You've been selected for a special offer. Click here to claim your prize!" -> Prediction: 1.0

Email: "Congratulations! You've won a free ticket." -> Prediction: 0.0

Email: "Hi, can we schedule a meeting tomorrow?" > Prediction: 0.0

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