Supervised Learning - Classification Week 9 Challenge

unable to open the attachment✅ 1. VBA to Apply Conditional Formatting & Freeze PanesSub FormatAndFreeze()    With ActiveSheet        ' Freeze top row        .Rows("2:2").Select        ActiveWindow.FreezePanes = True         …

✅ Summary of Actions:a. Data Formatting Apply borders Font size: 11 or 12 Center alignment b. Summary Statistics for Product Sales Total, Average, Std Dev, Variance, Max, Min of SALES c. Pivot Table: ProductLine vs Statusd. Add Custom Tab Named "ABC" with common formatting tools(Will be simulated by creating a separate…

Dataset not getting openedProject Overview (5-6 lines)Humans have an incredible ability to analyze images quickly and extract meaningful information effortlessly. However, machines do not inherently possess this capability and require image processing techniques to interpret visual data. This project aims to classify fashion…

Next Steps: Handle missing values (replace categorical missing values with the mode). Convert categorical data into numerical format for clustering. Apply K-Means Clustering to classify cars into groups. The Car Dataset has been successfully cleaned and encoded, making it ready for K-Means clustering.Next Steps: Normalize…

Next Steps: Handle missing values using appropriate imputation techniques. Standardize and clean categorical & numerical features. Perform descriptive analytics (summary statistics, distributions, etc.).The Automobile 1985 dataset has been successfully cleaned, and there are no missing values remaining.Next Steps:…

The Car Dataset has been successfully cleaned and encoded, making it ready for K-Means clustering.Next Steps: Normalize numerical features to ensure proper clustering. Apply K-Means clustering with an optimal number of clusters. Analyze business insights from the clusters.The Elbow Method plot shows that the optimal number…

1. What is a Neural Network?A Neural Network (NN) is a computational model inspired by the human brain, designed to recognize patterns and make decisions. It consists of: Input Layer: Takes in raw data. Hidden Layers: Processes data using weights and biases. Output Layer: Produces the final prediction. Neural networks…

1. Applying K-Nearest Neighbors (KNN) to the "Surface Defects in Stainless Steel Plates" DatasetI will now: Preprocess the dataset (scaling features, handling missing values). Apply KNN classifier with an optimal K value. Evaluate performance using accuracy, precision, recall, and F1-score. Compare results with SVM to…

1. Pros and Cons of SVM (Support Vector Machine) ✅ Advantages of SVM: Effective in High-Dimensional Spaces: Works well when the number of features is large. Robust to Overfitting: Especially useful in small datasets where the number of samples is less than the number of features. Works with Non-Linear Data: By using kernel…

Perform Gradient Descent in Python with any Loss FunctionGradient descent is an optimization algorithm used to minimize a loss function by iteratively adjusting the model parameters. Below is an example of implementing Gradient Descent for Linear Regression with Mean Squared Error (MSE) Loss Function. Python Implementationimport…

1) Calculate all 4 business moments using the given data (First Image)The four business moments are: Mean (First Moment) Variance (Second Moment) Skewness (Third Moment) Kurtosis (Fourth Moment) We will calculate them using the given random variable X and probabilities P(X).Step 1: Calculate Mean (Expected Value, E[X])The…

1. Why is there a difference in the formula of variance for population and sample?The variance formula differs for a population and a sample to account for the fact that a sample is only a subset of the population. When we estimate the variance from a sample, we need to correct for bias in our estimate. The sample variance…

Steps to Follow:Preprocess the data: Clean the data, encode categorical variables, and scale the features.Apply Principal Component Analysis (PCA): To reduce dimensionality and identify the most important features contributing to country classifications.K-Means Clustering: To group countries into clusters based on the…

To predict whether a patient has heart disease based on clinical parameters, we'll proceed by applying the following machine learning models:Logistic RegressionK-Nearest Neighbors (KNN) ClassifierSupport Vector Machine (SVM)Decision Tree ClassifierRandom Forest ClassifierWe will follow these steps:Data Preprocessing: Load…

1. Display the data of white wine and red wineThe dataset does not contain wine type data (white or red). Thus, this question is not applicable to the current dataset.2. Get the size of both white wine and red wineThe dataset contains 1338 rows and 7 columns in total.3. Get the number of null values in both types of winesThere…

1. Explain the data. Check if it is supervised or unsupervised. Demonstrate.Data Explanation: The data includes features about wines such as fixed acidity, volatile acidity, citric acid, residual sugar, alcohol content, and more. The target variable is the quality of the wine, which is an integer rating.Supervised/Unsupervised:…

-- Project 2: Alumni Career Choices Analysis-- 1. Create new schema as alumniCREATE SCHEMA IF NOT EXISTS alumni;USE alumni;-- 2. Import all .csv files into MySQL-- Import the CSV files into the following tables:-- College_A_HS, College_A_SE, College_A_SJ, College_B_HS, College_B_SE, College_B_SJ-- This step is done using…

-- 1. Create new schema as ecommerceCREATE SCHEMA IF NOT EXISTS ecommerce;USE ecommerce;-- 2. Import .csv file users_data into MySQL-- Right click on ecommerce schema -> Table Data import Wizard -> Give path of the file -> Next -> choose options: -- Create a new table, select delete if exist -> next…

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