🧩 Building a Machine Learning project is like solving a puzzle. Each step must seamlessly fit into the next to unlock insights from data. But the important thing is how those steps are lined up. In this post, I have explained how we should start a Machine Learning project as a beginner. From data preprocessing to model training and evaluation, I'll walk you through each stage, offering insights and best practices.
🔍 Using real-world data from the Titanic tragedy, let's explore how a logistic regression algorithm can predict passenger survival based on various features. Discover the power of this method in predicting outcomes and gain insights into interpreting the results effectively.
Here's the roadmap I followed👇🏻
💡 Analyze Dataset - Identifying the dataset and its context sets the stage for understanding the problem.
🔍 Describe Dataset - Understanding the characteristics and structure of the dataset.
🗑️ Dropping Columns - Streamlining focus by removing irrelevant columns.
➕ Create Column - Engineering new features to enhance model performance.
🔍 Checking NULL Values - Ensuring data integrity by handling missing values.
✅ Filling Missing Values - Strategically imputing missing data to maintain dataset completeness.
🔍 Outlier Detection - Identifying and addressing outliers to prevent skewed results.
📊 Feature Scaling - Normalizing features to ensure uniformity in model training.
🔢 Data Encoding - Converting categorical data into numerical format for model compatibility.
🧮 Feature Selection - Choosing relevant features to optimize model performance.
⚖️ Data Balancing - Addressing class imbalances to prevent biased model predictions.
🔄 Pre-processed Dataset - Ensuring the dataset is ready for model training.
➖ Data Splitting - Dividing data into training, validation, and testing sets for evaluation.
🧠 Model Training - Utilizing machine learning algorithms to train predictive models.
🔄 Modification using K-Fold Cross-Validation for Fine-tuning model performance.
🔄 Modification Add Hyper Parameter Tuning for Enhancing model performance.
📈 Performance Evaluation - Assessing model accuracy, precision, recall, and F1-score to gauge effectiveness.
For the source code👇🏻
https://lnkd.in/eWwMg5kJ
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