Collection of google colaboratory notebooks from Ultimate Data Science Bootcamp
| List | Link | Description |
|---|---|---|
| 1. | Dog breed Prediction | Convolutional Neural Network capable of identifying the breed of a dog in a supplied image. |
| 2. | Traffic Sign Classification | Convolutional Neural Network to build train and test a traffic sign classification model. Using tensorflow and keras. It is a multiclass classification problem. |
| 3. | Optical charactors Recognition UsingImage | text from images. After extracting the text we will apply some basic functions of OpenCV on that text to enhance it and to get more accurate results. |
| 4. | Plant Deseases Prediction | Convolutional Neural Network which will be able to predict whether a plant is suffering from a disease. |
| 5. | Vehicle Detect Counts | Detecting and counting vehicles in a given image or a video. |
| 6. | Face Swap | Extract faces of human-beings from a given image. We will use a pretrained model to extract landmarks from the faces. |
| 7. | Bird Species Prediction | Convolutional Neural Network which will be able to predict species of the bird. Use different layers and other hyperparameters for building, training and testing this multiclass classifictaion model. |
| 8. | Intel Image Classification | Convolutional neural network and train it on this images. This is a multi class classification problem and use Keras. |
| 9. | Language Translation using IBM Watson | Python will be used to capture the text, after which it will be sent to the IBM cloud to be translated into the chosen language. Watson AI will then be used to turn the translated text into a speech, and the file will then be output. |
| 10. | Laptop Price Predictor | Price variations and laptop types Prediction. |
| 11. | Course Recommendation System | Course Recommendation System using Udemy Dataset. Cosine and Linear Similarity. |
| 12. | EDA on udemyDataset | Exploratory Data Analysis on UdemyDataset. |
| 13. | IPL Win Probability Predictor | Indian Premier League (IPL) Win Probability Predictor. |
| 14. | Book Genre Prediction | Book generate Predictions. |
| 15. | Sentiment LG | Sentiment analysis of Logistic Regression. |
| 16. | Attrition Rate | Employee Attrition Rate Prediction. |
| 17. | Pokemon | Pokemon for Data Mining and Machine Learning. |
| 18. | Cat vs Dogs | The project classification cat an dog using the ImageDataGenerator class from keras.preprocessing.image to load and preprocess images for training the model. |
| 19. | Shopping Intentions | "Shopping Intentions" is a machine learning project that aims to predict whether an online shopper will complete a purchase on an e-commerce website. |
| 20. | Gender Prediction | Predict the gender of a person based on their voice characteristics. The code plots histograms of the independent variables for male and female samples to check for any visible differences between the two classes. |
| 21. | Location based Recommendation | Location-based recommendation using the K-Means algorithm with the Yelp dataset from Kaggle. The code then generates a countplot and a barplot of the number and names of the top 20 restaurants with the highest review count and stars. |
| 22. | Happiness | The happiness of countries. The data set is stored in a file called "Happiness.csv" and is read into a pandas DataFrame called "happy_data". The code then checks for missing values in the data and handles. The happiness of countries. The data set is stored in a file called "Happiness.csv" and is read into a pandas DataFrame called "happy_data". The code then checks for missing values in the data and handles. |
| 23. | Forest Fires | Forest fires. It starts by mounting Google Drive and reading in the 'fire_archive.csv' file as a Pandas DataFrame called 'forest'. It then checks for missing values, Gets descriptive statistics of the data, and plots a heatmap of the correlations between variables.creates dummy variables for the 'type' column and renames some of the columns for clarity. |
| 24. | Car selling Price | Defines a few functions to extract certain information from the 'torque', 'mileage', 'engine', and 'max_power' columns, such as the maximum rpm or maximum engine size in cc. These extracted values are then added to the 'cars' DataFrame as new columns. The original columns are then dropped. The training and test sets are then standardized using the StandardScaler function from scikit-learn. |
| 25. | Affairs | About people's affairs. The dataset has various columns, such as age, years married, number of children, education level, and occupation.generates a heatmap to visualize the correlation between different columns in the dataset. |
| 26. | Mushrooms | Project to classify mushrooms as either edible or poisonous based on various characteristics of the mushrooms such as their cap shape, color, and odor. |
| 27. | Google Apps | Preparing a dataset of Google Play Store app data for further analysis or modeling. It reads in the data from a CSV file, performs some data cleaning and preprocessing steps such as handling missing values and converting data types, and outputs a cleaned and preprocessed version of the dataset. |