{"id":35572,"date":"2025-02-23T09:44:59","date_gmt":"2025-02-23T04:14:59","guid":{"rendered":"https:\/\/tocxten.com\/?page_id=35572"},"modified":"2025-02-23T10:16:28","modified_gmt":"2025-02-23T04:46:28","slug":"machine-learning-process","status":"publish","type":"page","link":"https:\/\/tocxten.com\/index.php\/machine-learning-process\/","title":{"rendered":"Machine Learning Process"},"content":{"rendered":"\n
\"\"<\/figure>\n\n\n\n

Machine learning follows a structured process to develop models that can make predictions or automate decision-making. Let\u2019s explore each step as illustrated in block diagram in detail with an example where a bank wants to predict whether a customer will default on a loan or not.<\/strong><\/p>\n\n\n\n

\n\n\n\n

1. Understand the Business<\/strong><\/h3>\n\n\n\n

Before building a machine learning model, it is crucial to understand the business problem. This step involves discussions with stakeholders, defining objectives, and identifying the key performance indicators (KPIs).<\/p>\n\n\n\n

\u2705 Example:<\/strong>
A bank faces financial losses due to loan defaults. The goal is to build a predictive model<\/strong> that can classify whether a new loan applicant is likely to default or not. By identifying high-risk customers, the bank can take precautionary measures such as offering smaller loans or requiring higher credit scores.<\/p>\n\n\n\n

\n\n\n\n

2. Understand the Data<\/strong><\/h3>\n\n\n\n

Once the problem is defined, the next step is to explore and analyze the available data. This involves checking the dataset\u2019s features, distributions, and relationships.<\/p>\n\n\n\n

\u2705 Example:<\/strong>
The bank collects customer data with features such as:<\/p>\n\n\n\n

    \n
  • Age<\/strong> (Numerical)<\/li>\n\n\n\n
  • Income<\/strong> (Numerical)<\/li>\n\n\n\n
  • Loan Amount<\/strong> (Numerical)<\/li>\n\n\n\n
  • Credit Score<\/strong> (Numerical)<\/li>\n\n\n\n
  • Past Loan Repayment History<\/strong> (Categorical: Paid\/Defaulted)<\/li>\n<\/ul>\n\n\n\n

    The dataset is analyzed to find patterns. For example, customers with low credit scores and high loan amounts<\/strong> might have a higher tendency to default.<\/p>\n\n\n\n

    \n\n\n\n

    3. Data Preprocessing<\/strong><\/h3>\n\n\n\n

    Raw data is often incomplete, inconsistent, or noisy. Data preprocessing ensures the data is clean and suitable for modeling.<\/p>\n\n\n\n

    \u2705 Example:<\/strong><\/p>\n\n\n\n

      \n
    • Handling Missing Values:<\/strong> If some customers have missing credit scores, we fill them with the median credit score.<\/li>\n\n\n\n
    • Encoding Categorical Data:<\/strong> Converting categorical data into numerical form (e.g., \u201cPaid\u201d \u2192 0, \u201cDefaulted\u201d \u2192 1).<\/li>\n\n\n\n
    • Feature Scaling:<\/strong> Standardizing numerical data (e.g., normalizing income and loan amounts).<\/li>\n\n\n\n
    • Splitting Data:<\/strong> The dataset is divided into training data (80%)<\/strong> and testing data (20%)<\/strong> for model evaluation.<\/li>\n<\/ul>\n\n\n\n
      \n\n\n\n

      4. Modelling<\/strong><\/h3>\n\n\n\n

      In this step, we select a machine learning algorithm and train it using historical data. Common models for classification include Logistic Regression, Decision Trees, Random Forest, and Neural Networks<\/strong>.<\/p>\n\n\n\n

      \u2705 Example:<\/strong>
      The bank decides to use Logistic Regression<\/strong>, a classification algorithm, to predict loan defaults. The model learns relationships between the input variables (credit score, loan amount, income, etc.) and the target variable (default or not).<\/p>\n\n\n\n

      \n\n\n\n

      5. Model Evaluation<\/strong><\/h3>\n\n\n\n

      After training the model, we evaluate its performance using different metrics. This ensures the model generalizes well to unseen data.<\/p>\n\n\n\n

      \u2705 Example:<\/strong>
      The bank evaluates its logistic regression model using:<\/p>\n\n\n\n

        \n
      • Accuracy:<\/strong> Measures how many predictions were correct.<\/li>\n\n\n\n
      • Precision & Recall:<\/strong> Useful for imbalanced datasets where one class (default) is less frequent.<\/li>\n\n\n\n
      • Confusion Matrix:<\/strong> Helps understand the number of true positives, true negatives, false positives, and false negatives.<\/li>\n<\/ul>\n\n\n\n

        Suppose the model has an accuracy of 85%<\/strong>, meaning it correctly predicts loan defaults in 85 out of 100 cases. If the recall is low, the bank may retrain the model using additional data or a different algorithm.<\/p>\n\n\n\n

        \n\n\n\n

        6. Model Deployment<\/strong><\/h3>\n\n\n\n

        A well-performing model is deployed into production, where it is used to make real-world decisions.<\/p>\n\n\n\n

        \u2705 Example:<\/strong>
        The bank integrates the model into its loan approval system. When a customer applies for a loan, the model analyzes their details and predicts whether they are likely to default (1) or not default (0)<\/strong>.<\/p>\n\n\n\n

          \n
        • If the model predicts default (1)<\/strong>, the bank may reject the loan or impose stricter conditions.<\/li>\n\n\n\n
        • If the model predicts not default (0)<\/strong>, the bank can approve the loan.<\/li>\n<\/ul>\n\n\n\n

          The model is continuously monitored, and periodic retraining is performed to improve its accuracy over time.<\/p>\n\n\n\n

          \n\n\n\n

          Conclusion<\/strong><\/h3>\n\n\n\n

          The machine learning process is iterative. If a model performs poorly, we go back to data preprocessing, feature engineering, or model selection<\/strong> to improve it. In our loan default prediction example, machine learning helps the bank reduce financial risk, improve decision-making, and optimize loan approvals efficiently<\/strong><\/p>\n\n\n\n

          <\/p>\n","protected":false},"excerpt":{"rendered":"

          Machine learning follows a structured process to develop models that can make predictions or automate decision-making. Let\u2019s explore each step as illustrated in block diagram in detail with an example…<\/p>\n","protected":false},"author":1,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"om_disable_all_campaigns":false,"_monsterinsights_skip_tracking":false,"_monsterinsights_sitenote_active":false,"_monsterinsights_sitenote_note":"","_monsterinsights_sitenote_category":0,"footnotes":"","_links_to":"","_links_to_target":""},"class_list":["post-35572","page","type-page","status-publish","hentry"],"post_mailing_queue_ids":[],"_links":{"self":[{"href":"https:\/\/tocxten.com\/index.php\/wp-json\/wp\/v2\/pages\/35572","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/tocxten.com\/index.php\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/tocxten.com\/index.php\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/tocxten.com\/index.php\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/tocxten.com\/index.php\/wp-json\/wp\/v2\/comments?post=35572"}],"version-history":[{"count":3,"href":"https:\/\/tocxten.com\/index.php\/wp-json\/wp\/v2\/pages\/35572\/revisions"}],"predecessor-version":[{"id":35578,"href":"https:\/\/tocxten.com\/index.php\/wp-json\/wp\/v2\/pages\/35572\/revisions\/35578"}],"wp:attachment":[{"href":"https:\/\/tocxten.com\/index.php\/wp-json\/wp\/v2\/media?parent=35572"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}