Inspiration
What it does
How we built itProject: Stock Price Prediction
I created a simple stock price prediction project using Python and several machine learning techniques. In this project, I aimed to predict stock prices for different tickers, including AAPL, GOOG, MSFT, and NFLX, using historical data.
Data Preprocessing I started by loading the historical stock price data from a CSV file and replacing the ticker names with numerical values to prepare the data for modeling. After that, I removed the 'Volume' column from the dataset and split it into features (x) and the target (y).
Data Scaling To ensure that the features are on a similar scale, I used the MinMaxScaler to scale the feature data between 0 and 1. Scaling the data can help improve the performance of machine learning models.
Exploratory Data Analysis (EDA) I performed a basic EDA by creating scatter plots to visualize the relationship between each feature and the target variable (Adjusted Close price). This allowed me to gain insight into how the features might influence stock prices.
Linear Regression Model I used a Linear Regression model to make predictions. I split the data into training and testing sets, with a test size of 1%, and trained the model using the training data. After training the model, I made predictions on the test data and calculated various evaluation metrics, including Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and R-squared (R2) to assess the model's performance.
Support Vector Regression (SVR) In addition to Linear Regression, I also implemented a Support Vector Regression (SVR) model. The SVR model is a powerful technique for regression tasks. I followed similar steps to preprocess the data, split it, train the model, and evaluate its performance using the same metrics.
K-Nearest Neighbors (KNN) Regression K-Nearest Neighbors (KNN) is a non-parametric regression technique. I used KNN regression to predict stock prices and calculated the Mean Squared Error (MSE) to assess model performance. I also conducted a grid search to find the optimal number of neighbors (K) for KNN regression.
Visualizing KNN Results I visualized the MSE values for different K values in KNN regression to find the most suitable number of neighbors. This helped in understanding how the choice of K affects the model's performance.
The project involved preprocessing the data, implementing and evaluating multiple machine learning models, and performing data visualization to gain insights into the relationships between features and stock prices. The choice of model and its performance may vary for different stocks, so this project serves as a foundation for stock price prediction and can be further expanded and fine-tuned.
User What languages, frameworks, platforms, cloud servicesProject: Stock Price Prediction I created a simple stock price prediction project using Python and several machine learning techniques. In this project, I aimed to predict stock prices for different tickers, including AAPL, GOOG, MSFT, and NFLX, using historical data.
Data Preprocessing I started by loading the historical stock price data from a CSV file and replacing the ticker names with numerical values to prepare the data for modeling. After that, I removed the 'Volume' column from the dataset and split it into features (x) and the target (y).
Data Scaling To ensure that the features are on a similar scale, I used the MinMaxScaler to scale the feature data between 0 and 1. Scaling the data can help improve the performance of machine learning models.
Exploratory Data Analysis (EDA) I performed a basic EDA by creating scatter plots to visualize the relationship between each feature and the target variable (Adjusted Close price). This allowed me to gain insight into how the features might influence stock prices.
Linear Regression Model I used a Linear Regression model to make predictions. I split the data into training and testing sets, with a test size of 1%, and trained the model using the training data. After training the model, I made predictions on the test data and calculated various evaluation metrics, including Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and R-squared (R2) to assess the model's performance.
Support Vector Regression (SVR) In addition to Linear Regression, I also implemented a Support Vector Regression (SVR) model. The SVR model is a powerful technique for regression tasks. I followed similar steps to preprocess the data, split it, train the model, and evaluate its performance using the same metrics.
K-Nearest Neighbors (KNN) Regression K-Nearest Neighbors (KNN) is a non-parametric regression technique. I used KNN regression to predict stock prices and calculated the Mean Squared Error (MSE) to assess model performance. I also conducted a grid search to find the optimal number of neighbors (K) for KNN regression.
The project involved preprocessing the data, implementing and evaluating multiple machine learning models, and performing data visualization to gain insights into the relationships between features and stock prices. The choice of model and its performance may vary for different stocks, so this project serves as a foundation for stock price prediction and can be further expanded and fine-tuned.
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