ABDULHAMEED IDRIS ADEDAMOLA

How I Fixed Missing @mipmap/ic_launcher_background in Android Studio (Complete Guide) ✅ While working on my Android project, I encountered an issue where: @mipmap/ic_launcher_background was being referenced, but the file didn’t exist in my project. This caused confusion and potential build issues. Here’s the exact solution that worked for me. 🔍 Understanding the Issue In Android development, @mipmap/ic_launcher_background is not something you manually type or guess. It refers to a real resource file inside your project: res/mipmap/ic_launcher_background If this file is missing, your launcher icon setup is incomplete. ⚠️ What NOT to Do I initially considered selecting assets from this path: C:\Program Files\Android\Android Studio\plugins\android\resources\images\asset_studio This is incorrect ❌ These are internal Android Studio assets They are not meant for app development use Using them won’t properly fix your project ✅ The Correct Solut...

ROC Curve Analysis (Model Performance Evidence) The ROC (Receiver Operating Characteristic) curve illustrates the performance of the Decision Tree classifier in distinguishing between the two classes: Poor and Fair credit scores. Evidence of Model Performance AUC Score: 0.75 Random Classifier Baseline: A diagonal reference line representing no discrimination ability (AUC = 0.50) As shown in the ROC curve, the model’s performance curve lies consistently above the diagonal baseline, indicating that it performs significantly better than random guessing. Interpretation An AUC value of 0.75 indicates that the model has a 75% probability of ranking a randomly chosen positive instance higher than a randomly chosen negative instance. In other words, the classifier demonstrates acceptable to good discriminatory performance for a baseline Decision Tree model. Conclusion: The ROC curve confirms that the model outperforms a random classifier...

AI/ML Practical Task 3 Progress AI/ML Practical Task 3 Progress Today, I started working on Task 3 of my AI/ML practical assignment using Google Colab. The goal of this task is to improve a credit risk classification model using feature selection and evaluation techniques. Dataset Overview The dataset used is the Credit Risk Dataset , which contains various financial and behavioral features used to predict credit score classification. Initial Setup I began by loading the dataset into Google Colab and preparing the data for analysis. The features and target variables were defined as follows: Features (X): Input variables derived from the dataset Target (y): Credit_Score_Coded (classification label) The dataset was then split into training and testing sets using a 60/40 ratio . Train-Test Split X_train, X_test, y_train, y_test = train_test_split( features, target, test_size=0.4, random_state=42 ) Next Steps The next phase of the tas...

🌟 How the Hierarchy Star Reports App Was Submitted for Google Play Review 🌟 Hello everyone 👋, The Hierarchy Star Reports app has successfully been submitted for Google Play review! This guide explains exactly how it was prepared for production: Step 1: Complete Closed Testing A closed test was conducted with selected users to ensure the app was stable and all features worked as intended. Feedback was collected and applied as needed. Step 2: Prepare the Production Release In Google Play Console: Navigate to Test and release → Production Click Create new release Select the existing .aab file from the library (no changes after the closed test) Step 3: Add Release Notes Release notes were entered to inform users: <en-US> Production release — same build as closed test, no changes. </en-US> Step 4: Review and Save After confirming the app bundle and release notes, Save was clicked. A warning about a deobfuscation file appeared, but this is optio...

🌳 Hyperparameter Tuning in Decision Trees In machine learning, building a powerful model isn’t just about choosing the right algorithm—it’s also about tuning its hyperparameters . For decision trees, these settings control how the tree grows and how well it generalizes to new data. Below are some of the most important hyperparameters and how they affect your model: 🔹 max_depth This parameter defines the maximum depth (number of levels) of the decision tree. A deeper tree can learn complex patterns but may lead to overfitting . A shallower tree may not capture enough patterns, leading to underfitting . Default value: None This means the tree will keep growing until: All leaves are pure (contain only one class), or Each leaf has fewer samples than min_samples_split. 🔹 min_samples_split This determines the minimum number of samples required to split an internal node . Smaller values → more splits → more complex model Larger values → fewe...

Credit Risk Prediction Using Decision Trees (3MTT Assignment Task 2) This project focuses on developing a predictive model for classifying credit risk using a dataset of 100,000 customer records and 24 features. The goal is to predict whether a customer has a Poor or Standard credit score using Decision Tree classifiers. Step 1: Import and Explore Dataset The dataset includes features like Age, Occupation, Annual Income, Monthly Balance, Credit Mix, and Payment Behaviour. After exploring, there were no missing values . The dataset is fairly balanced: 53% Standard and 47% Poor . A balanced dataset is crucial as it ensures that the model does not favor one class over the other and produces reliable predictions for both “Poor” and “Standard” credit scores. Step 2: Transform Categorical Features Categorical attributes such as Month, Occupation, Credit Mix, Payment of Minimum Amount, and Payment Behaviour were numerically encoded for the Decision Tree. Feature ...