Decision trees are a famous technique in machine learning because of their understandability and effective utilization for categorization and regression related tasks. Our machine learning framework is different which highlights us from others our research service comprises of an attractive abstract, research proposal, methodology we use and final conclusion. In depth research will be conducted to attain the desired results. Here we  discuss various research concepts based on scholars’ curiosity where we utilize decision trees and their integration techniques such as Gradient Boosted Trees and Random Forests.

1. Customer Churn Forecasting for Telecom:

• Goal: We forecast whether the consumer will leave the telecom service or not in the upcoming billing process.
• Dataset: Our work considers complaint data, service utilization data, billing patterns and consumer utilization data.
• Model: For best accuracy, we employ Random Forest and Decision Tree methods.

2. Sentiment Analysis of Customer Reviews:

• Goal: Our main goal is to categorize customer feedback into various sentiment categories as positive, negative or neutral.
• Dataset: We utilize text format data taken from customer feedback on social media environments and websites.
• Model: For baseline model, Decision Tree method is utilized by us and after we extract features from text data, we enhance with ensemble techniques.

3. Fraud identification in Transactions:

• Goal: We aim to identify illegitimate transactions for finance related associations.
• Dataset: Our approach considers labeled transaction data by denoting illegal activities.
• Model: Because of its complicated nature, we make use of Gradient Boosted Trees or Random Forest to achieve greatest efficiency.

4. Predictive Maintenance in Manufacturing:

• Goal: By considering sensor information and functional metrics, we forecast the tool failures.
• Dataset: Our work utilizes machine logs, maintenance data and sensor information.
• Model: To attain effective forecasting performance, we employ Gradient Boosted trees.

5. Image Categorization for Plant Diseases:

• Goal: By analyzing plant leaves images, we detect plant diseases.
• Dataset: We make use of labeled image datasets related to plant diseases.
• Model: By utilizing image processing techniques, we extract the features and utilize Random Forest approach.

6. Forecasting Stock Market Movements:

• Goal: By considering the previous data, we forecast whether the stock price will increase or decrease for the upcoming days.
• Dataset: In this, we use previous stock rates, potentially augmented with various characteristics such as macroeconomic indicators or trading volume.
• Model: For feature importance analysis, our approach suggests decision trees, and to minimize overfitting issue, we use random forest.

7. E-commerce Product Suggestion:

• Goal: We suggest goods or products to customers in terms of their previous search and purchase data.
• Dataset: Our work considers ratings, customer activity data, previous purchase data and product catalog.
• Model: To examine purchasing trends, we utilize decision trees and employ some ensemble methods for suggestion models.

8. Loan Approval Forecasting:

• Goal: Based on the user’s data, we forecast whether a loan must be sanctioned or not.
• Dataset: We utilize financial related data that comprises debt-to-income ratio, loan money, previous credit data, etc.
• Model: To achieve best accuracy, our work proposes methods like Random Forest and Decision Tree.

9. Disease Diagnosis:

• Goal: By considering patient’s historical information and symptoms, we diagnose a disease.
• Dataset: We make use of clinical data like diagnosis, previous clinical information, symptoms and patient demographics.
• Model: Our project utilizes various ensemble techniques for efficient forecasting and to visualize diagnosis path, we use Decision Trees.

10. Real Estate Price Forecasting:

• Goal: In terms of various features and location, we forecast the selling rate of houses.
• Dataset: We consider House based information such as location, facilities, number of rooms and square footage.
• Model: Our aim is to build a baseline framework using Decision Tree and to enhance the efficiency, we employ Boosted Trees.

Procedures to Follow for Decision Tree Research Concepts:

1. Data Gathering: If required, we collect and utilize dataset from several resources.
2. Preprocessing of Data: Our project focused on the preprocessing steps including cleaning the dataset, managing missing values and encode categorical attributes.
3. Exploratory Data Analysis (EDA): In this, we interpret the data dispersions, connections and formats.
4. Feature Engineering: To enhance the framework’s efficiency, our approach aims to design new features.
5. Training of Model: By utilizing Python libraries such as Scikit-learn, we train our decision tree framework.
6. Model Evaluation: For regression tasks, our work considers several metrics like precision, accuracy, F1-score, recall or RMSE/MSE.
7. Hyperparameter Tuning: By adjusting parameters such as min samples split, min samples leaf and max depth, we optimize our framework.
8. Visualization: To interpret the decision-making procedure, our project visualizes the tree.
9. Ensemble Techniques: If required, we enhance the efficiency of our framework by employing methods like Gradient Boosting or Random Forest.
10. Deployment: Implement our framework by utilizing web applications or cloud-based service if our approach is expected for production.

We note that the decision tree techniques are sensitive to overfitting even though they are simple to understand. By utilizing approaches such as pruning and adjusting the appropriate hyperparameters, it is very significant to balance the complications of the tree with the efficiency on unseen data. Be at ease your Research Article will be crafted well by our writers and we do publish in leading journal. Work privacy will be maintained.