Description

---

Leveraging historical data, macroeconomic indicators, LSTM and Prophet models for enhanced stock price forecasting. Analyze trends, patterns, and economic factors to gain insights and make data-driven predictions. Leverage advanced modeling techniques for reliable forecasts.

Features

---

• Historical Data Analysis: Gain insights from historical stock price data to identify trends, patterns, and correlations.
• Macroeconomic Indicators: Incorporate macroeconomic indicators to enhance the predictive capabilities of the models.
• LSTM Model: Utilize the LSTM model to capture long-term dependencies in stock price data and make accurate predictions.
• Prophet Model: Leverage the Prophet model for time series forecasting, taking into account seasonality and trends.
• Evaluation Metrics: Evaluate the performance of the models using metrics such as mean squared error (MSE) and mean absolute error (MAE).

Dataset

---

The project uses historical stock price data of Apple Inc. (AAPL) as an example, but it can be easily adapted to any other stock. The dataset includes daily stock prices, volume, and other relevant attributes. You can replace the dataset with your desired stock data to perform analysis and predictions.

Read more

---

The jupyter notebook which features explanations is available on GitHub.

GitHub

Introduction

---

Financial transaction fraud poses a significant threat to organizations and individuals. ML-FinFraud-Detector offers an effective solution to identify fraudulent activities, helping enhance financial security. By leveraging machine learning techniques, this project can analyze transaction data and classify transactions as either fraudulent or legitimate.

Features

---

• Utilizes the XGBoost algorithm for robust fraud detection
• Incorporates precision-recall and ROC curves for performance evaluation
• Feature importance analysis to identify influential factors
• Helps organizations prevent financial losses and enhance security

Dataset

---

The Bank Account Fraud (BAF) suite of datasets, recently published at NeurIPS 2022, offers a comprehensive collection of synthetic bank account fraud tabular datasets. This suite comprises six distinct datasets, making it a valuable resource for evaluating both novel and existing machine learning (ML) and fair ML methods in the field of fraud detection.

Read more

---

The jupyter notebook which features explanations is available on GitHub.

GitHub

Description

---

FinHeadlines-MoodTracker is an NLP project that performs sentiment analysis on financial news headlines. It aims to predict the sentiment (positive, negative, or neutral) associated with the news headlines and track the overall mood in the financial market. The project utilizes a combination of Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) layers for sentiment classification.

Features

---

The project includes the following components:

• Data preprocessing: The financial news headlines are preprocessed to remove noise, tokenize the text, and perform other necessary cleaning steps.
• Word embedding: The project uses pre-trained word embeddings to represent words as dense vectors and capture semantic relationships.
• Sentiment analysis model: The model consists of an embedding layer, 1D convolution, max pooling, bidirectional LSTM, dropout, and dense layer for sentiment classification.
• Training and evaluation: The model is trained on a labeled dataset of financial news headlines and evaluated using appropriate metrics.
• Mood tracking: The model's predictions are used to track the overall sentiment or mood in the financial market.

Dataset

---

The dataset was originally introduced and described in a research paper titled "Good debt or bad debt: Detecting semantic orientations in economic texts" by Malo, P., Sinha, A., Takala, P., Korhonen, P., and Wallenius, J. (2014). The paper was published in the Journal of the American Society for Information Science and Technology.

Read more

---

The jupyter notebook which features explanations is available on GitHub.

GitHub

Description

---

Loan Default Predictor is a machine learning project that aims to predict loan defaults using various algorithms such as Logistic Regression, Random Forest, and XGBoost. The project includes data preprocessing, model training, feature analysis, and making informed lending decisions based on the predictions.

Features

---

You can find more information in this article.

Article

Dataset

---

The project utilizes a dataset containing loan information such as borrower details, loan amount, interest rate, credit score, employment history, etc. The data is provided in a CSV format and should be placed in the data directory of this repository.

Read more

---

The jupyter notebook which features explanations is available on GitHub.

GitHub

Description

---

The main goal of this project is to build a linear regression model to predict the number of wins for each team in a Premier League season. Additionally, we want to identify the key factors that contribute to a team becoming a champion. By doing so, we aim to gain insights into what makes a team successful in the Premier League.

Features

---

• Data Acquisition and Exploration: Reading and exploring the datasets to understand the available data.
• Data Preprocessing: Cleaning and preparing the data for further analysis.
• Exploratory Data Analysis (EDA): Visualizing and analyzing the data to gain insights into the trends and patterns.
• Model Building: Constructing a linear regression model to predict the number of wins and identifying important features.
• Conclusion: Summarizing the findings and key takeaways from the analysis.

Dataset

---

The project utilizes several datasets related to the Premier League:

• results.csv: Contains information about all the match results in the Premier League between the 2006/07 season and the 2017/18 season.
• stats.csv: Expands on the statistical categories that are official for the Premier League and will be used for analysis.
• EPL standings 2000-2022.csv: Provides the standings of the individual teams throughout the seasons 2000-2022.
• with_goalscorers.csv: Provides the top goalscorers from the formation of the Premier League in 1992 until now.

Read more

---

The jupyter notebook which features explanations is available on GitHub.

GitHub