output.json

{
    "Implement a basic K-Means Clustering algorithm from scratch using Python.": 3,
    "Write a function in Python that determines the best value of k (number of clusters) using the Elbow Method.": 3,
    "Given a dataset, apply feature scaling and run K-Means Clustering using scikit-learn.": 2,
    "Create a Python script to visualize the results of K-Means Clustering on a 2D dataset.": 2,
    "Script a program to compare the performance of different initialization methods for centroids.": 3,
    "Write code to compute the silhouette coefficient for evaluating the clustering quality.": 3,
    "Implement a mini-batch K-Means clustering using Python.": 4,
    "Write a Python function to identify the centroid of a new data point in an existing K-Means model.": 2,
    "Using Pandas and Python, clean and prepare a real-world dataset for K-Means Clustering.": 3,
    "Create a multi-dimensional K-Means clustering example and visualize it using PCA for dimensionality reduction.": 4,
    "Write a Python function to implement K-NN from scratch on a simple dataset.": 3,
    "Use scikit-learn to demonstrate K-NN classification using the Iris dataset.": 1,
    "Implement a LazyLearningClassifier in Python that uses K-NN under the hood.": 4,
    "Create a Python script to visualize the decision boundary of K-NN on a 2D dataset.": 3,
    "Develop a weighted K-NN algorithm in Python and test its performance against the standard K-NN.": 4,
    "Optimize a K-NN model in a large dataset using approximate nearest neighbors techniques like LSH or kd-trees.": 5,
    "Given a dataset with time-series data, how would you apply K-NN for forecasting?": 4,
    "Implement a logistic regression model from scratch using Python.": 3,
    "Write a function that calculates the Gini impurity for a given dataset in a Decision Tree.": 2,
    "Code a Support Vector Machine using scikit-learn to classify data from a toy dataset.": 2,
    "Create a k-NN classifier in Python and test its performance on a sample dataset.": 2,
    "Use a Boosting algorithm to improve the accuracy of a weak classifier on a dataset.": 4,
    "Implement a function for feature scaling and normalization in preparation for classification.": 2,
    "Develop a Python script that visualizes the decision boundary of a given classification model.": 3,
    "Write code to add, subtract, and multiply two matrices without using external libraries.": 1,
    "Implement a function to calculate the transpose of a given matrix.": 1,
    "Code to find the determinant of a matrix using recursion.": 3,
    "Develop a Python function to compute the inverse of a matrix.": 3,
    "Write an algorithm to perform eigenvalue and eigenvector decomposition.": 5,
    "Create a Python script to solve a system of linear equations using NumPy.": 5,
    "Implement a function to calculate the L2 norm of a vector.": 1,
    "Write a program to verify if a given square matrix is orthogonal.": 3,
    "Implement simple linear regression from scratch in Python.": 3,
    "Implement a multiple linear regression model using NumPy or similar libraries.": 5,
    "Write a Python function that performs the gradient descent algorithm for linear regression.": 3,
    "Create a Python script to calculate the VIF for each predictor in a dataset.": 5,
    "Code a Python function to implement ridge regression using scikit-learn.": 5,
    "Use pandas to load a dataset and prepare it for linear regression, handling any missing values.": 3,
    "Plot residual diagrams and analyze the model fit using Matplotlib or Seaborn.": 3,
    "Write a Python function to compute and print out model evaluation metrics (RMSE, MAE, R-squared).": 3,
    "Perform a polynomial regression on a sample dataset and plot the results.": 3,
    "Use scikit-learn to perform cross-validation on a linear regression model and extract the test scores.": 5,
    "Implement a linear regression model to predict customer lifetime value using scikit-learn.": 5,
    "Develop a regularized regression model to analyze and predict healthcare costs.": 5,
    "Perform a time-series linear regression analysis on stock market data.": 5,
    "Create a Python script that tunes the hyperparameters of an elastic net regression model using grid search.": 5,
    "Write a Python function that incorporates polynomial features into a regression model for better fit and analyzes the trade-off with model complexity.": 5,
    "Implement basic Gradient Descent to minimize a simple quadratic function.": 2,
    "Write a Python function to perform SGD on a sample dataset.": 3,
    "Code a simulation in Python demonstrating the effects of different learning rates on convergence.": 3,
    "Implement the Momentum technique in a Gradient Descent optimizer.": 3,
    "Create a regularization function in Python that penalizes large weights in a linear regression model.": 3,
    "Develop a Python script that uses the Adam optimizer from a library like TensorFlow or PyTorch.": 2,
    "Write a function that showcases the difference between L1 and L2 regularization on a small dataset.": 4,
    "Implement PCA on a given dataset using scikit-learn and plot the explained variance ratio.": 3,
    "Write a Python function that performs feature selection using Recursive Feature Elimination (RFE).": 4,
    "Code a small example to demonstrate the use of LDA for classification.": 3,
    "Implement a basic version of an autoencoder for dimensionality reduction using TensorFlow/Keras.": 4,
    "Modify a given t-SNE implementation to work more efficiently on a large-scale dataset.": 5,
    "Develop a Python script to compare the performance of PCA and LDA on a sample dataset.": 4,
    "Create a Python function that uses Factor Analysis for dimensionality reduction on multivariate data.": 4,
    "Write a code snippet to perform feature extraction using Non-negative Matrix Factorization (NMF).": 4,
    "Use the feature importance provided by a trained ensemble model to reduce the dimensionality of a dataset in Python.": 4,
    "Implement a basic linear SVM from scratch using Python.": 4,
    "Write a Python function to select an optimal C parameter for an SVM using cross-validation.": 3,
    "Code an SVM model in scikit-learn to classify text data using TF-IDF features.": 2,
    "Develop a multi-class SVM classifier on a given dataset using the one-vs-one strategy.": 3,
    "Use Python to demonstrate the impact of different kernels on SVM decision boundaries with a 2D dataset.": 3,
    "Implement an SVM in Python using a stochastic gradient descent approach.": 4,
    "Write a script to visualize support vectors in a trained SVM model.": 2,
    "Create a Python function for grid search optimization to find the best kernel and its parameters for an SVM.": 3,
    "Write a Python function that calculates the probability of rolling a sum of 'S' on two dice.": 1,
    "Implement a function that simulates a biased coin flip n times and estimates the probability of heads.": 1,
    "Code a Gaussian Naive Bayes classifier from scratch using Python.": 3,
    "Simulate the Law of Large Numbers using Python: verify that as the number of coin tosses increases, the average of the results becomes closer to the expected value.": 1,
    "Write a Python script that estimates the mean and variance of a dataset and plots the corresponding Gaussian distribution.": 2,
    "Write Python code to calculate mean, median, and mode from a given list of numbers.": 1,
    "Generate and visualize 1,000 random points from a Normal distribution in Python.": 2,
    "Implement a simple linear regression model from scratch in Python.": 3,
    "Simulate the Monty Hall problem in Python and analyze the results.": 2,
    "Create a Python function to perform a t-test given two sample datasets.": 3,
    "Write a Python script to compute and graphically display a correlation matrix for a given dataset.": 3,
    "Implement the Metropolis-Hastings algorithm for a simple Bayesian inference simulation.": 4,
    "Create a Python program that estimates Pi using a Monte Carlo simulation.": 2,
    "Write a Python code snippet for performing a Chi-squared test of independence on a contingency table.": 3,
    "Develop a Python function to convert a non-stationary time series into a stationary one.": 4,
    "Write an R script to conduct an ANOVA test on a given dataset.": 4,
    "Implement PCA for dimensionality reduction on a high-dimensional dataset in Python.": 4,
    "Implement a Python function that calculates the Mean Squared Error between predicted and actual values.": 2,
    "Write a Python code snippet to compute the Cross-Entropy loss given predicted probabilities and actual labels.": 3,
    "Implement a gradient descent algorithm in Python to minimize a simple quadratic cost function.": 3,
    "Create a Python simulation that compares the convergence speed of batch and stochastic gradient descent.": 4,
    "Build a Python class that implements an adaptive learning rate algorithm, like Adam or AdaGrad, from scratch.": 5,
    "Write a Python function that minimizes a cost function using simulated annealing.": 4,
    "Implement a basic version of the RMSprop optimization algorithm in Python.": 4,
    "Write a Python implementation of basic gradient descent to find the minimum of a quadratic function.": 2,
    "Implement batch gradient descent for linear regression from scratch using Python.": 3,
    "Create a stochastic gradient descent algorithm in Python for optimizing a logistic regression model.": 4,
    "Simulate annealing of the learning rate in gradient descent and plot the convergence over time.": 4,
    "Design a Python function to compare the convergence speed of gradient descent with and without momentum.": 3,
    "Implement gradient descent with early stopping using Python.": 3,
    "Code a mini-batch gradient descent optimizer and test it on a small dataset.": 3,
    "Write a Python function to check the gradients computed by a gradient descent algorithm.": 2,
    "Experiment with different weight initializations and observe their impact on gradient descent optimization.": 3,
    "Implement and visualize the optimization path of the Adam optimizer vs. vanilla gradient descent.": 4
}