Client Background

Client: A leading IT & tech firm in the USA

Industry Type: IT

Products & Services: IT Consulting, IT Support, SaaS

Organization Size: 200+

The Problem

To improve the accuracy to 90%+ where the current models which were built were giving an accuracy of 58%. The existing predictive models are currently achieving an accuracy of 58%, which is insufficient for meeting the desired performance benchmarks. The objective is to enhance the accuracy of these models to exceed 90%. This requires a comprehensive evaluation and improvement of the model development process, including data quality, feature engineering, algorithm selection, and hyperparameter tuning. The challenge lies in identifying and implementing effective strategies to significantly boost the model’s predictive accuracy while ensuring robustness and generalizability.

Our Solution

Our solution architecture is designed to efficiently preprocess financial data, perform feature selection, and train a GRU (Gated Recurrent Unit) model for predictive analysis. The architecture comprises the following components:

Data Preprocessing: Involves loading the dataset from a CSV file, checking for null values, and performing initial exploratory data analysis (EDA).

Feature Selection: Utilizes correlation analysis to identify relevant features and drop irrelevant ones.

Model Building: Constructs a GRU model using TensorFlow’s Keras API, consisting of multiple GRU layers followed by dropout regularization and a dense output layer with a sigmoid activation function.

Model Evaluation: Evaluates the trained model on test data to measure its performance.

Solution Architecture

Deliverables

Custom Python scripts for data preprocessing, feature selection, and model building.

Trained GRU model saved to a file for future use.

Tech Stack

• Tools used
• pandas, scikit-learn, TensorFlow, Keras, seaborn, matplotlib
• Language/techniques used
• <Python programming, data preprocessing, feature selection, deep learning
• Models used
• GRU (Gated Recurrent Unit)
• Skills used
•  Data preprocessing, feature engineering, model building, evaluation
• Databases used
• Multiplexer 

What are the technical Challenges Faced during Project Execution

Handling data variability: Dealing with varying data formats and distributions across different datasets.

Feature selection: Identifying relevant features and discarding irrelevant ones to improve model performance.

Model optimization: Tuning hyperparameters and optimizing the architecture of the GRU model for better accuracy and generalization.

How the Technical Challenges were Solved

The solution provides a reliable framework for analyzing financial data and making predictions, aiding in strategic decision-making for the business.

By accurately predicting financial trends and performance metrics, the solution enables proactive measures to be taken to optimize operations and maximize profitability.

Business Impact

The solution provides a reliable framework for analyzing financial data and making predictions, aiding in strategic decision-making for the business.

By accurately predicting financial trends and performance metrics, the solution enables proactive measures to be taken to optimize operations and maximize profitability.

Project Snapshots 

Project Video

https://www.loom.com/share/bed67661fa7540d2ab39705096c4591d?sid=91b0bdb7-ca4b-43ab-8d81-aaff7ed4a679

Summarize

Summarized: https://blackcoffer.com/

This project was done by the Blackcoffer Team, a Global IT Consulting firm.

Contact Details

This solution was designed and developed by Blackcoffer Team
Here are my contact details:
Firm Name: Blackcoffer Pvt. Ltd.
Firm Website: www.blackcoffer.com
Firm Address: 4/2, E-Extension, Shaym Vihar Phase 1, New Delhi 110043
Email: ajay@blackcoffer.com
Skype: asbidyarthy
WhatsApp: +91 9717367468
Telegram: @asbidyarthy