Description: Create a game of rock, paper, scissors using Python. Train a machine learning model to recognize hand gestures and predict the next move of the player. You can use Tensorflow to build a Convolutional Neural Network (CNN) model that recognizes rock-paper-scissors signs in a photo.
Solution: Use Python and Tensorflow to build the CNN model. Preprocess the input images, train the model on a dataset of rock-paper-scissors images, and implement the game logic.
Skills: Python, Tensorflow, CNN, Image Classification
Description: Build an image classifier using a Convolutional Neural Network (CNN) to distinguish between images of cats and dogs. Use the Keras library to build the model and train it on a dataset of cat and dog images.
Solution: Use Python and Keras to create the CNN model. Preprocess the input images, train the model on a labeled dataset of cat and dog images, and evaluate its performance.
Skills: Python, Keras, CNN, Image Classification
Description: Develop a book recommendation engine using the k-nearest neighbors (KNN) algorithm. Use the Pandas library to preprocess the data and the Scikit-learn library to build the model.
Solution: Use Python, Pandas, and Scikit-learn to implement the KNN algorithm. Preprocess the book data, build the KNN model, and provide recommendations based on user preferences.
Skills: Python, Pandas, Scikit-learn, KNN, Recommendation Systems
Description: Build a linear regression model to predict health insurance costs based on various factors such as age, BMI, smoking habits, etc. Use the Pandas library to preprocess the data and the Scikit-learn library to build the model.
Solution: Use Python, Pandas, and Scikit-learn to create the linear regression model. Preprocess the health data, train the model on a labeled dataset, and calculate insurance costs based on input variables.
Skills: Python, Pandas, Scikit-learn, Linear Regression
Description: Construct a neural network model to classify SMS text messages as spam or not spam. Use the Pandas library to preprocess the data and the Keras library to build the model.
Solution: Use Python, Pandas, and Keras to build the neural network model. Preprocess the text data, train the model on a labeled dataset of SMS messages, and classify new messages as spam or not spam.
Skills: Python, Pandas, Keras, Neural Networks, Text Classification
Analyze demographic data using Pandas. Given a dataset extracted from the 1994 Census database, perform analysis to answer questions such as race representation, average age of men, and percentage of people with advanced education earning more than 50K.
Solution: Use Pandas to load and analyze the dataset, perform aggregations, apply filters, and compute statistics.
Skills and tools learned: Python, Pandas, data analysis, data manipulation.
Create a function that calculates the mean, variance, standard deviation, max, min, and sum of a 3x3 matrix. Implement the function using Numpy and return the results as a dictionary.
Solution: Write a Python function using Numpy to perform calculations on the matrix and return the results as a dictionary.
Skills and tools learned: Python, Numpy, matrix operations, function implementation.
Visualize and analyze medical examination data using Pandas, Seaborn, and Matplotlib. Explore the dataset containing patients' body measurements, blood test results, and lifestyle choices to understand the relationship between cardiac disease and various factors.
Solution: Load the dataset using Pandas, clean and preprocess the data, visualize the relationships using Matplotlib and Seaborn, and make calculations based on the data.
Skills and tools learned: Python, Pandas, Seaborn, Matplotlib, data visualization, exploratory data analysis.
Visualize time series data using line charts, bar charts, and box plots. Analyze the dataset of daily page views on the freeCodeCamp.org forum to identify patterns, yearly and monthly growth, and overall trends.
Solution: Use Pandas to load and preprocess the dataset, create time series visualizations using Matplotlib and Seaborn, and derive insights from the data.
Skills and tools learned: Python, Pandas, Matplotlib, Seaborn, time series analysis, data visualization.
Analyze book sales data using Pandas. Explore the dataset that includes information about book titles, authors, publishers, and sales. Answer questions such as the highest average sales by an author, the publisher with the highest total sales, and the book with the highest sales.
Solution: Load and analyze the book sales dataset using Pandas, perform aggregations, apply filters, and derive insights from the data.
Skills and tools learned: Python, Pandas, data analysis, data manipulation.
Create a bar chart using D3.js to display the GDP of the United States from 1947 to 2015. Fetch the data using JSON and use D3.js to create the chart. The chart should include a title, x-axis label, y-axis label, and tooltip displaying additional information on hover.
Solution: Use D3.js to fetch and visualize the data as a bar chart with appropriate axes, labels, and interactivity.
Skills and tools learned: D3.js, data visualization, JSON data fetching.
Create a scatterplot graph using D3.js to display the relationship between the number of doping allegations in professional cycling and the finishing time of the top 35 cyclists in the Tour de France from 1994 to 2014. Fetch the data using JSON and use D3.js to create the graph. The graph should include a title, x-axis label, y-axis label, and tooltip displaying additional information on hover.
Solution: Use D3.js to fetch and visualize the data as a scatterplot graph with appropriate axes, labels, and interactivity.
Skills and tools learned: D3.js, data visualization, JSON data fetching.
Create a heat map using D3.js to display the monthly global land-surface temperature from 1753 to 2015. Fetch the data using JSON and use D3.js to create the heat map. The heat map should include a title, x-axis label, y-axis label, and tooltip displaying additional information on hover.
Solution: Use D3.js to fetch and visualize the data as a heat map with appropriate axes, labels, and color encoding.
Skills and tools learned: D3.js, data visualization, JSON data fetching.
Create a choropleth map using D3.js to display the educational attainment by county in the United States. Fetch the data using JSON and use D3.js to create the choropleth map. The map should include a title, legend, and tooltip displaying additional information on hover.
Solution: Use D3.js to fetch and visualize the data as a choropleth map with appropriate color encoding, legend, and interactivity.
Skills and tools learned: D3.js, data visualization, JSON data fetching.
Create a treemap diagram using D3.js to display the top 100 highest grossing movies. Fetch the data using JSON and use D3.js to create the treemap diagram. The diagram should include a title and tooltip displaying additional information on hover.
Solution: Use D3.js to fetch and visualize the data as a treemap diagram with appropriate hierarchy and interactivity.
Skills and tools learned: D3.js, data visualization, JSON data fetching.
Create a tribute page using HTML and CSS. The page should include an image, text, and a link to an external website. Make sure the page is responsive and adjusts to different screen sizes.
Solution: Use HTML and CSS to build a tribute page with a responsive design that adapts to different screen sizes.
Skills learned: HTML, CSS, responsive web design.
Create a survey form using HTML and CSS. The form should include various types of input fields such as text boxes, radio buttons, and checkboxes. Ensure that the form is responsive and adjusts to different screen sizes.
Solution: Use HTML and CSS to build a survey form with different input fields and a responsive design.
Skills learned: HTML, CSS, form elements, responsive web design.
Create a product landing page using HTML and CSS. The page should include a header, navigation bar, product information section, and a form to collect user information. Ensure that the page is responsive and adjusts to different screen sizes.
Solution: Use HTML and CSS to build a product landing page with a responsive design that includes a navigation bar, product section, and a form.
Skills learned: HTML, CSS, navigation bars, forms, responsive web design.
Create a technical documentation page using HTML and CSS. The page should include a header, navigation bar, and sections of technical documentation. Ensure that the page is responsive and adjusts to different screen sizes.
Solution: Use HTML and CSS to build a technical documentation page with a responsive design that includes a navigation bar and sections of documentation.
Skills learned: HTML, CSS, documentation layout, responsive web design.
Create a personal portfolio webpage using HTML and CSS. The page should include a header, navigation bar, sections for projects and skills, and a contact form. Ensure that the page is responsive and adjusts to different screen sizes.
Solution: Use HTML and CSS to build a personal portfolio webpage with a responsive design that includes sections for projects, skills, and a contact form.
Skills learned: HTML, CSS, portfolio layout, responsive web design.