Ravit Jain

Are you planning to get into Data Science field? This GIF should help you to understand different branches of Data Science!

Data science is a field that uses data to solve problems and make predictions. It is a vast and complex field that requires a variety of skills, including:

- Data preprocessing: This is the process of cleaning and preparing data for analysis

- Software engineering: This involves using programming languages and software tools to develop and maintain data science projects

- Web development: This is the process of creating and maintaining websites that can be used to present data science findings

- Statistics: This is the use of mathematical tools to analyze data

- Programming languages: This includes languages like Python, R, and SQL, which are used to write code for data science projects

- Machine learning: This is a type of artificial intelligence that allows computers to learn from data without being explicitly programmed

- Deep learning: This is a subset of machine learning that uses artificial neural networks to learn from data

- Soft skills: These are non-technical skills that are important for data scientists, such as communication, teamwork, and problem-solving

Follow our The Ravit Show Newsletter with 34000+ Data Science/AI professionals! Link in the comments!

How does the inner workings of a Machine Learning model work?

INCOME and LOAN are the two essential variables we explore with this model.

The goal is to predict the income of individuals based on various input features, including age, sex, and a score, along with their loan status.

Here's a step-by-step breakdown of how we build the model:

Initial Dataset: We start with a dataset containing information about individuals, including their income, loan status, age, sex, and score.

Exploratory Data Analysis (EDA): We perform thorough EDA to gain insights into the data, spot patterns, and identify potential challenges.

Data Cleaning: We ensure the dataset is free from errors, inconsistencies, and missing values.

Data Curation: Redundant features are removed to streamline the dataset and enhance model performance.

Pre-Processed Dataset: We preprocess the data using techniques like PCA (Principal Component Analysis) and LDA (Linear Discriminant Analysis) to reduce dimensionality and extract meaningful features.

Use as Training Set: After preprocessing, we split the dataset into a training set and a test set. The training set is used to teach the model patterns and relationships in the data.

Learning Algorithms & Hyperparameter Optimization: We apply various learning algorithms, such as SVM (Support Vector Machine), LR (Logistic Regression), KNN (K-Nearest Neighbors), DT (Decision Trees), and RF (Random Forest). The hyperparameters of these models are fine-tuned using grid search to achieve optimal performance.

Feature Selection: We select the most relevant features that significantly impact the outcome to avoid overfitting and improve interpretability.

Cross-Validation Model: To validate the model's robustness, we employ cross-validation techniques to measure its generalization performance.

Trained Model & Predicted Y values: Once the model is trained, we use the test set to make predictions on unseen data.

Evaluation Metrics: We evaluate the model's performance using various metrics like classification accuracy, sensitivity, specificity, MCC (Matthews Correlation Coefficient), RMSE (Root Mean Squared Error), MSE (Mean Squared Error), and R² (R-squared) for regression tasks.

Regression: We explore the relationship between input features and income through regression analysis.

Evaluate Model Performances: We assess how well our model performs on the given dataset and make necessary adjustments if needed.

Additional Models: We experimented with Random Search and Gradient Boosting (GB) to compare their performances with RF.

The final model is ready to make predictions on new data, helping us gain insights into the income levels of individuals and their loan status.

Machine Learning offers limitless opportunities, and we are thrilled to leverage this model for meaningful real-world applications!

Follow The Ravit Show Newsletter link in the comments!

10 Free Python Courses to upskill in 2023!

CS50's Introduction to Programming with Python -- https://lnkd.in/dzTekpHp

Python Tutorials for Absolute Beginners by CS Dojo -- https://lnkd.in/d39TnzKr

Python Full Course - Learn Python in 12 Hours -- https://lnkd.in/dbes94ft

Crash Course on Python -- https://lnkd.in/dJZjy36k

Introduction To Python Programming -- https://lnkd.in/dEnZ-DWv

Python from Beginner to Intermediate in 30 min -- https://lnkd.in/dGaqiFrs

Automate the Boring Stuff with Python -- https://lnkd.in/dTbrBy4B

The Python Handbook – Learn Python for Beginners -- https://lnkd.in/d-Uza29V

Python Core -- https://lnkd.in/dYD-gEtd

Python Fundamentals for Beginners -- https://lnkd.in/dWB8EpVU

For more such valuable Data Science / AI Content. Don't forget to subscribe to The Ravit Show -- https://bit.ly/3pe02Xc