Dr. Hana Rabbouch’s Post

🚀 Sharing a Milestone: Built My First Machine Learning Model! https://lnkd.in/dEmxB-Dm I’m thrilled to share my journey of building my very first machine learning model. This experience has been incredibly rewarding, as it allowed me to dive into the end-to-end process of an ML project. 💡 Steps I Followed: 1️⃣ Loaded and Inspected the Data: Explored the dataset to understand its structure, identify patterns, and spot any anomalies. 2️⃣ Cleaned and Prepared the Data: Removed duplicates, handled missing values, and ensured the data was ready for analysis. 3️⃣ Prepared the Data for Modeling: Split the data into features and labels, and created training and test sets for evaluation. 4️⃣ Built and Trained the Model: Leveraged [model name, e.g., Random Forest] to learn from the data. 5️⃣ Made Predictions: Tested the model with unseen data to evaluate its predictive performance. 6️⃣ Evaluated Accuracy: Used metrics like [accuracy, precision, recall, etc.] to assess how well the model performed. 🔍 Keynotes: Data preprocessing is the backbone of any ML project—it sets the foundation for everything else. Testing different models and tuning hyperparameters is crucial for achieving better results. Splitting data correctly ensures the model is both accurate and reliable for future predictions. This journey reinforced the importance of structured problem-solving in machine learning. I’m excited to tackle more complex challenges and continue learning in this fascinating field! Have you recently started your ML journey or have tips to share? Let’s connect and exchange ideas! T21 Services Bolatito Sarumi Dr. ‘Bosun Tijani Samuel Odudu #3MTT #MachineLearning #DataScience #Python #LearningJourney

MODEL EVALUATION Model evaluation is a prerequisite for model development. This technique allows us to assess the performance of our model and tell an appropriate story about our findings. Models are prone to both overfitting and underfitting. Model evaluation lets us determine whether our model is overfitting or underfitting. Depending on the project we are working on, we can evaluate our model using different evaluation metrics. For supervised machine learning, which is one of the most prevalent types of machine learning, there are two subtypes: classification supervised machine learning and regression machine learning. When working on a classification-supervised machine learning project, we have two binary classes (true or false, yes or no, 0 or 1). There are numerous machine learning algorithms that are appropriate for training our model in this regard. We have classifiers such as Logistic Regression, Decision Tree, and Random Forest. Most beginners in the field of machine learning believe that the logistic regression algorithm should be employed for regression-supervised machine learning, which is incorrect. Metrics for evaluating the classification machine learning model: ✔️ ROC Curve and AUC Score ✔️ Precision ✔️ Accuracy ✔️ Recall (Sensitivity) ✔️ F1 Score Evaluation metrics for regression-supervised machine learning models: ✔️ Mean Absolute Error (MAE) ✔️ Mean Squared Error (MSE) ✔️ Root Mean Squared Error (RMSE) ✔️ R2 Score (Coefficient of Determination) ✔️ Mean Absolute Percentage Error (MAPE) #Datascience #machinelearning #modelevaluation #datascientist #tech #python #programming

📊🔍 Unlock Insights with Correlation Analysis Pearson Correlation Coefficient: Measures the linear relationship between numerical variables (Pearson's r). Spearman Rank Correlation: Non-parametric test for monotonic relationships. Kendall's Tau: Non-parametric measure for ranked variables. Variable Types: Applies to continuous, ordinal, and dichotomous variables. Applications: Feature selection, multicollinearity detection, and model evaluation. Limitations: Correlation ≠ causation, linearity assumption, outlier sensitivity. Enhancing ML Models: Informing feature engineering and algorithm selection. Unlock Insights: Make Informed Decisions. #datascience #tech #code #technology #business #ml #statistics #AI #dataanalysis #correlation #python #medium https://lnkd.in/g85iPnvS

Cross Validation in Machine Learning: A Comprehensive Guide 💥💥 GET FULL SOURCE CODE AT THIS LINK 👇👇 👉 https://lnkd.in/dbc4JqvA Cross validation is a crucial technique in machine learning that allows us to evaluate the performance of a model on unseen data. It's an essential step in ensuring that our models generalize well to new, unseen data. In this video, we will delve into the concept of cross validation, its types, and its application in machine learning. Cross validation involves training a model on a subset of the data and evaluating its performance on the remaining subset. This process is repeated multiple times, with different subsets used for training and testing each time. This approach helps to reduce overfitting and provides a more accurate estimate of the model's performance. We will explore the different types of cross validation, including k-fold cross validation, leave-one-out cross validation, and stratified cross validation. We will also discuss the importance of hyperparameter tuning and how cross validation can be used in conjunction with hyperparameter tuning to improve model performance. Additional Resources: * This video uses Python and scikit-learn libraries for implementation of cross validation. #MachineLearning #CrossValidation #DataScience #ArtificialIntelligence #stemeducation #stemcareer #stemjobs #Python #scikit-learn #AIforAll #MLforAll Find this and all other slideshows for free on our website: https://lnkd.in/dbc4JqvA #MachineLearning #CrossValidation #DataScience #ArtificialIntelligence #stemeducation #stemcareer #stemjobs #Python #scikit-learn #AIforAll #MLforAll https://lnkd.in/dCFt4fu6

Hello Connections, Addressing Class Imbalance in Machine Learning Model In the realm of machine learning, handling data imbalances is crucial for building robust and fair models. Class imbalance, where the number of instances of one class significantly outweighs the other, can lead to biased predictions and an overall decrease in model performance. To address this issue, I have implemented and evaluated several techniques for redressing class imbalance in Python. Why It Matters Class imbalance is a common problem in various domains, including fraud detection, medical diagnosis, and customer churn prediction. Failing to address this issue can lead to models that are excellent at predicting the majority class but poor at detecting the minority class, which is often of greater interest. What You'll Find in the Notebook **Redressal Techniques Implemented**:   - **Resampling Techniques**: Both oversampling the minority class and undersampling the majority class.      - **Advanced Methods**: Implementation of Synthetic Minority Over-sampling Technique (SMOTE). Thanks to the mentor Ajay Kumar Gupta sir sudhanshu kumar Vishwa Mohan Krish Naik Priya Bhatia Ekta Negi PW Skills iNeuron.ai #machinelearning #datascience #dataanalysis #classimbalance #redressalmethods   

12 𝐓𝐲𝐩𝐞𝐬 𝐨𝐟 𝐀𝐥𝐠𝐨𝐫𝐢𝐭𝐡𝐦𝐬 𝐘𝐨𝐮 𝐌𝐮𝐬𝐭 𝐊𝐧𝐨𝐰... Understanding different algorithms is key to solving computational problems. ➤ Brute Force Algorithms: Exhaustively check all possibilities. Simple but time-consuming for large datasets. ➤Divide and Conquer Algorithms: Break problems into smaller sub-problems, solve independently, and combine solutions. ➤Greedy Algorithms: Make locally optimal choices aiming for a global optimum. Simple and efficient. ➤Dynamic Programming Algorithms: Solve by breaking problems into overlapping sub-problems and storing results to reduce time complexity. ➤Randomized Algorithms: Use random elements, offering faster solutions on average. ➤Backtracking Algorithms: Systematically search all possible solutions, abandoning paths that cannot be extended. ➤Heuristic Algorithms: Provide feasible, not always optimal, solutions. Used in optimization problems. ➤Sorting Algorithms: Arrange elements in order. Examples include Bubble Sort and Quick Sort. ➤Searching Algorithms: Find the position of a target value. Examples include Linear Search and Binary Search. ➤Graph Algorithms: Solve problems related to graphs (nodes and edges). Examples include Dijkstra's and BFS. ➤Machine Learning Algorithms: Used in ML models, including supervised, unsupervised, and reinforcement learning. ➤Cryptography Algorithms: Ensure secure communication. Examples include AES and RSA. Gif credit: Nelson Djalo Follow me for more programming tips!! Suraj Singh✅ #algorithms #datastructure #jobs

visualization always helpful to understand everything

The machine learning course on the Great Learning platform typically covers the fundamentals of machine learning, including supervised and unsupervised learning, algorithms like regression, classification, clustering, and neural networks. The course often includes hands-on projects, practical applications, and real-world case studies to help reinforce the concepts. Additionally, it may introduce tools and libraries commonly used in the field, such as Python, TensorFlow, and Scikit-learn, enabling learners to implement machine learning solutions effectively.

What an incredible journey it has been mentoring Ramesh Kumar Avudaikumar on his path through the world of Machine Learning. Witnessing his evolution from crafting simple Python scripts to analyzing complex customer data has been nothing short of inspiring. Reposting Ramesh's achievements as a beacon of inspiration for others His dedication is a testament to the power of learning as the best investment one can make in themselves. The road from beginner to expert is a journey worth embarking on. Collaboration with National University of Singapore and Emeritus Ariba Imam Deepraj Fernandes #MachineLearning #AI #database #Python #DataScience #ContinuousLearning #Mentorship #successstory

🌟 Thrilled to unveil my latest project! 🌟 I'm excited to share my journey of building and deploying Customer Churn Prediction using Artificial Neural Networks (ANN). This project is a testament to how advanced AI can empower businesses to make data-driven decisions and tackle one of the most pressing challenges in customer relationship management: churn prediction. 📌 Project Details: Objective: To predict customer churn with high accuracy, enabling businesses to take proactive steps in retaining customers. Tech Stack: Python, TensorFlow, Pandas, NumPy, Streamlit for deployment. Data Insights: Performed extensive exploratory data analysis and feature engineering to enhance model performance. Model: Designed and optimized a multi-layer ANN that delivers precise predictions while maintaining scalability. 🌐 Deployment: You can experience the Customer Churn Prediction App live here: https://lnkd.in/ghWu9kMp 📂 GitHub Repository: Check out the code and methodology behind this project: https://lnkd.in/gDh8-t76 💡 Why this project matters: Customer churn has significant implications for profitability in competitive markets. By leveraging predictive analytics, businesses can: 1️⃣ Identify at-risk customers. 2️⃣ Understand churn patterns. 3️⃣ Tailor retention strategies for impactful results. 🎉 Acknowledgments: This project wouldn’t have been possible without the mentorship and inspiration of Krish Naik. His guidance has been instrumental in deepening my understanding of AI and its practical applications. Thank you, Krish, for being a constant source of motivation and knowledge! 🙏 #MachineLearning #DeepLearning #ArtificialIntelligence #CustomerRetention #Python #DataScience #Mentorship #KrishNaik