Building a Data Mining Model from Scratch: 5 Key Steps, Tools & Best Practices

Data mining involves identifying patterns in large, unstructured datasets—such as browsing activity, transaction logs, or sensor readings—and turning them into actionable insights. For example, it can predict purchasing behavior based on historical sales data or flag system failures by detecting anomalies in machine logs. 

These models help businesses make faster, data-informed decisions and automate responses in high-stakes environments. This shift has contributed to a 36% growth in data science job openings between 2023 and 2033, significantly outpacing the average for all occupations. 

If you're aiming to build data mining models that deliver tangible results, a structured approach is essential.  This guide covers five key steps in building a data mining model, practical tools to use, and best practices to ensure long-term performance.

 Building a Data Mining Model from Scratch: Step-by-Step Guide

Creating a data mining model from scratch starts with collecting and preparing the right data, then moves into selecting algorithms, training the model, and evaluating performance. Each step builds on the last, and skipping one can lead to inaccurate or incomplete insights.

Different techniques, like decision trees, clustering, association rules, and neural networks, serve different purposes depending on your goals and data type. These models power everything from fraud detection systems to recommendation engines.

But building effective models isn’t just about tools—it also means avoiding common mistakes like training on duplicate records, using future data in your features, or relying solely on accuracy in imbalanced datasets.

These issues can quietly skew results, make models unreliable in production, and lead to misleading business decisions.

In the sections below, we’ll walk you through each step in detail, with practical tips and tools to guide your process.

Step 1: Define the Problem Statement

Before working with data, you need to define a clear, specific problem your model will solve. Without this, you risk building a model that’s technically sound but useless in practice. 

A well-defined problem links your data mining efforts to real business outcomes: boosting ROI, automating decisions, or reducing risk. It also shapes every step that follows, from data selection to algorithm choice.

1. Identify the objective:
Pinpoint what you want the model to achieve based on business context. This ensures the model outputs are relevant and actionable.

• Examples:
  • Fraud detection in banking (binary classification)
  • Customer segmentation in e-commerce (clustering)
  • Inventory demand forecasting in retail (time series regression)
  • Sentiment analysis in product reviews (NLP classification)
• Tools & Techniques:
  • Business process analysis (BPMN tools, flowcharts)
  • Stakeholder interviews and requirement gathering
  • Data exploration tools (Power BI, Tableau, Python's pandas_profiling)
  • Problem framing templates (CRISP-DM, Lean Canvas)

2. Set model goals:
Define what success looks like in measurable terms. This guides how you evaluate the model later.

• Metrics to set:
  • Accuracy/Precision/Recall targets (e.g., 90% fraud detection accuracy)
  • Latency limits for real-time decisions (e.g., <100ms prediction time)
  • Scalability requirements (handle millions of records/day)
• Helpful Techniques:
  • Establish KPIs with domain experts
  • Use baseline models to set realistic benchmarks
  • Consider deployment constraints early (cloud compatibility, model interpretability)

Step 2: Data Collection & Preparation

No algorithm can fix poor data. If your dataset is noisy, inconsistent, or irrelevant, even the most advanced models will fail. This step lays the foundation: collecting the right data, cleaning it thoroughly, and selecting only the features that matter. Each decision here directly affects model performance, accuracy, and reliability.

1. Data Sources:

Choose sources based on your problem statement and data availability. Think about volume, variety, and access method.

• Internal Databases:
  • Use when: You need structured, historical business data like transactions, user activity, or product logs.
  • Examples: MySQL for customer orders; MongoDB for user sessions.
• Public Datasets:
  • Use when: Prototyping, benchmarking models, or when internal data is limited.
  • Examples: UCI ML Repo for classification tasks; Kaggle’s retail datasets for demand forecasting.
• APIs:
  • Use when: You need up-to-date or external data like financial markets, social sentiment, or weather.
  • Examples: Twitter API for sentiment analysis; Alpha Vantage for stock data.
• Web Scraping:
  • Use when: Data is only available on websites without APIs.
  • Tools: BeautifulSoup, Scrapy for collecting product prices, reviews, or competitor info.

2. Data Cleaning:

Raw data is messy. Clean it before training to avoid biased or broken models.

• Handle Missing Values:
  • When to apply: If key features are missing in more than 5-10% of rows.
  • Techniques: Mean/median imputation for numeric fields; mode for categorical; or deletion if missing completely at random.
  • Example: Fill missing age values in a customer dataset with median age.
• Remove Duplicates and Outliers:
  • When to apply: If repeated rows skew analysis or outliers distort model learning.
  • Techniques: drop_duplicates() in pandas; Z-score or IQR method for outlier analysis.
  • Example: Drop repeated entries in survey data; remove anomalous purchase amounts.
• Correct Inconsistent Formats and Errors:
  • When to apply: If formats vary (e.g., dates, currencies) or entries are inconsistent.
  • Techniques: Standardize using regex, mapping functions, or data parsing libraries.
  • Example: Convert “01/01/2025” and “2025-01-01” into a unified format.

3. Feature Selection:

Not all data is useful. Removing irrelevant or redundant features improves speed and accuracy.

• Identify Relevant Variables:
  • When to apply: Early, during exploratory analysis or after baseline modeling.
  • Techniques: Correlation heatmaps, mutual information, domain knowledge.
  • Example: Drop customer ID, keep age and location for churn prediction.
• Dimensionality Reduction:
  • When to apply: When you have too many features (high-dimensional data) or face multicollinearity.
  • Techniques:
    • PCA: Reduces dimensions while preserving variance.
    • Lasso Regression: Penalizes less important features to zero.
  • Example: Use PCA in image recognition; apply Lasso in linear regression models.

Also Read: Different Types of Regression Models You Need to Know

Step 3: Choosing the Right Algorithm

Selecting the right algorithm is about understanding the structure of your data, the type of problem you're solving, and the kind of output your model needs to produce. Picking the wrong approach can lead to poor accuracy, slow performance, or models that don’t generalize well. 

Consider whether your data is labeled, the size of your dataset, and whether interpretability or raw performance is more important for your application.

1. Supervised vs. Unsupervised Learning:

Start by identifying whether your data is labeled. This will determine the learning method and narrow your algorithm choices.

A: Supervised Learning
Used when your dataset includes labeled outcomes (i.e., you know what the correct answer looks like). Ideal for prediction tasks.

• Classification:
  • Examples:
    • Spam detection in email (spam or not spam)
    • Loan default prediction (yes/no)
  • Algorithms: Logistic Regression, Decision Trees, Random Forest
• Regression:
  • Examples:
    • Forecasting monthly sales
    • Predicting housing prices
  • Algorithms: Linear Regression, Gradient Boosting, Neural Networks

B: Unsupervised Learning
Used when your dataset lacks labeled outcomes. Ideal for exploring hidden patterns and groupings.

• Clustering:
  • Examples:
    • Customer segmentation for targeted marketing
    • Grouping products based on purchasing behavior
  • Algorithms: K-Means, DBSCAN, Hierarchical Clustering
• Association Rules:
  • Examples:
    • Market basket analysis (if user buys X, recommend Y)
    • Product bundling strategies
  • Algorithms: Apriori, FP-Growth

Below is a quick overview of commonly used algorithms, along with their ideal use cases, strengths, and limitations:

How to Choose the Right Fit?

Choosing the right algorithm means balancing technical capability with business constraints. Here's how to make that decision smarter:

• Have labeled data?
  • Use classification for predicting categories (e.g., churn/no churn).
  • Use regression for forecasting numbers (e.g., revenue, usage).
• Working with unlabeled data?
  • Choose clustering to group similar items (e.g., customer personas).
  • Use association rules to identify patterns in co-occurring items.
• Need explainability?
  • Go with Decision Trees or Logistic Regression—great for regulated industries where transparency matters.
• Handling messy or complex data?
  • Random Forest balances accuracy and noise tolerance.
  • Neural Networks shine when the patterns are deep and non-obvious, especially in unstructured data.
• Dealing with small, high-dimensional data?
  • SVM often performs well for things like text classification or sentiment analysis.

Also Read: Supervised vs Unsupervised Learning: Key Differences

Step 4: Model Training & Implementation

Once your data is ready, it’s time to train your model. This is where it begins to learn patterns, relationships, and rules from the data. Model training means feeding your cleaned and structured data into a machine learning algorithm so it can find patterns that generalize beyond what it has seen. 

A well-trained model doesn’t just memorize the data—it understands it well enough to make accurate predictions on unseen inputs.

Data Splitting: Train, Validate, Test

To make sure your model is reliable and not just memorizing the training data, you need to split your dataset into three parts:

• Training Set (60–70%)
  • When to use: Always used first to teach the model.
  • Why: This is where the model learns the patterns in your data.
  • Tip: The model never sees validation or test data during training.
• Validation Set (10–20%)
  • When to use: During model tuning and selection.
  • Why: Helps fine-tune hyperparameters and avoid overfitting by checking how the model performs on unseen data.
  • Tip: Not used for final evaluation—just for adjustment.
• Test Set (20%)
  • When to use: At the very end, once training and tuning are complete.
  • Why: Gives an unbiased estimate of how the model will perform in the real world.
  • Tip: Never touch this set during model building.

Hyperparameter Tuning Techniques

Hyperparameters are configuration settings that control the model's behavior—like the depth of a decision tree or the number of neurons in a neural network. Tuning them is key to maximizing performance.

• Grid Search
  • When to use: Small to medium-sized datasets with a manageable number of hyperparameters.
  • Why: It exhaustively searches through every possible combination.
  • Tool: GridSearchCV in Scikit-Learn.
  • Trade-off: Accurate but slow with many parameters.
• Random Search
  • When to use: When you have limited time or many hyperparameters.
  • Why: Selects a random subset of combinations, which can be surprisingly effective.
  • Tool: RandomizedSearchCV.
  • Trade-off: Faster, but might miss the best combination.
• Bayesian Optimization
  • When to use: Complex models with expensive training cycles.
  • Why: Uses probability models to find the best parameters with fewer evaluations.
  • Tools: Optuna, Hyperopt.
  • Trade-off: Smarter search, but requires setup and computation.

Implementation Tools

Choosing the right library depends on your model complexity, performance needs, and familiarity with tools.

• Scikit-Learn
  • When to use: For classic machine learning tasks like classification, regression, or clustering.
  • Why: Simple, fast, and widely used in production pipelines and research.
  • Great for: Logistic regression, random forests, SVMs.
• TensorFlow/Keras
  • When to use: For deep learning tasks involving unstructured data like images, text, or audio.
  • Why: Powerful, flexible, and production-ready.
  • Great for: Neural networks, LSTM models, CNNs, and large-scale deployments.

Example (Scikit-Learn):

from sklearn.ensemble import RandomForestClassifier
# Initialize the model with 100 decision trees
model = RandomForestClassifier(n_estimators=100)
# Train on the training data
model.fit(X_train, y_train)

You’ll typically iterate through multiple models, tuning and testing until you strike the right balance of performance, speed, and generalizability. This step is where all your earlier prep pays off—or exposes weaknesses.

Also Read: Recurrent Neural Networks: Introduction, Problems, LSTMs Explained

Step 5: Model Evaluation & Performance Tuning

Training a model isn’t the finish line. What matters is how well it performs on new, unseen data. Evaluation shows you whether your model is making useful predictions, while tuning helps you fix weak spots. This step ensures your model is reliable, scalable, and ready for real-world deployment.

Key Metrics: When and Why to Use Them

Different problems call for different evaluation metrics. Don't rely on a single score—use a combination to get a full picture.

• Accuracy
  • What it shows: The overall proportion of correct predictions.
  • Best for: Balanced datasets where false positives and false negatives are equally costly.
  • Limit: Misleading in imbalanced cases (e.g., fraud detection).
  • Tool: accuracy_score from sklearn.metrics
• Precision & Recall
  • Precision: How many predicted positives are truly positive.
  • Recall: How many actual positives were correctly identified.
  • Best for: Imbalanced datasets.
    • Example: Precision matters more in email spam filters; recall is crucial in medical diagnosis.
  • Tool: precision_score, recall_score
• F1-Score
  • What it shows: The balance between precision and recall.
  • Best for: Scenarios with uneven class distribution and a need for balance.
  • Tool: f1_score
• ROC Curve / AUC
  • What it shows: Trade-off between true positive and false positive rates.
  • Best for: Comparing classifier performance across different thresholds.
  • Tool: roc_auc_score, roc_curve

Common Issues: What Can Go Wrong—and How to Fix It

Even accurate models can fail if they generalize poorly or miss important signals. Here’s how to spot and fix that:

• Overfitting
  • Symptoms: High accuracy on training data, poor performance on validation/test data.
  • Fixes:
    • Regularization (L1, L2 penalties)
    • Prune decision trees or reduce layers in neural nets
    • Use K-fold cross-validation
    • Add dropout in deep learning model
• Underfitting
  • Symptoms: Low accuracy across both training and test data.
  • Fixes:
    • Use a more complex algorithm (e.g., switch from linear regression to random forest)
    • Add more relevant features (feature engineering)
    • Reduce regularization too if it's too strict

Optimization Techniques: Get the Best Out of Your Model

Once the basics are solid, these strategies can give your model a competitive edge.

• Feature Engineering
  • What it does: Transforms raw data into meaningful input that improves model performance.
  • Examples:
    • Extracting date features (e.g., day of week, holiday flag)
    • Creating ratios (e.g., spend per visit)
  • Tools: pandas, FeatureTools
• Hyperparameter Tuning
  • Why it matters: A few tweaks can significantly improve performance.
  • Best Practices:
    • Start with Random Search for speed
    • Move to Grid Search or Bayesian Optimization for refinement
  • Tools: GridSearchCV, Optuna, Hyperopt
• Ensemble Learning
  • What it does: Combines multiple models to reduce error and variance.
  • Methods:
    • Bagging: (e.g., Random Forest) reduces variance
    • Boosting: (e.g., XGBoost, LightGBM) reduces bias
    • Stacking: combines different models’ strengths
  • Best for: When individual models perform well but miss different aspects

Also Read: Data Preprocessing in Machine Learning: 7 Key Steps to Follow, Strategies, & Applications

Once your data mining model is built, the next step is measuring how well it actually performs.

How Do You Evaluate the Performance of a Data Mining Model?

Evaluating a data mining model is about more than just getting a high accuracy score. You need to know how well your model performs across different scenarios, how it generalizes to new data, and whether it can be trusted in real-world use.

1. Key Evaluation Metrics (And When to Use Them):

Different problems call for different metrics. Relying on just one can mislead you—especially with imbalanced or high-stakes data.

• Accuracy
  • What it measures: Overall percentage of correct predictions.
  • Best for: Balanced datasets.
  • Example: Classifying email categories (work, personal, spam) when each class has similar volume.
  • Caution: Useless if 95% of your customers are non-churn and the model predicts “non-churn” for everyone.
  • Tool: accuracy_score (Scikit-Learn)
• Precision
  • What it measures: How many predicted positives are truly positive.
  • Best for: When false positives are costly.
  • Example: Flagging a legitimate transaction as fraud frustrates users and hurts business.
  • Tool: precision_score
• Recall
  • What it measures: How many actual positives the model correctly identified.
  • Best for: High-risk domains where missing a positive is dangerous.
  • Example: In cancer screening, missing one case is far worse than a false alarm.
  • Tool: recall_score
• F1-Score
  • What it measures: Balance between precision and recall.
  • Best for: Imbalanced classes where you need a trade-off.
  • Example: Spam detection—where you don’t want to miss spam (recall) but also not flag legit emails (precision).
  • Tool: f1_score
• ROC Curve / AUC (Area Under Curve)
  • What it shows: The model’s ability to distinguish between classes at various thresholds.
  • Best for: Comparing classifiers or visualizing performance over decision boundaries.
  • Example: Comparing fraud models based on how confidently they can separate fraudulent and normal transactions.
  • Tool: roc_auc_score, roc_curve

2. Validation Techniques: Test It Before You Trust It

Proper validation helps you avoid overfitting and gives a realistic sense of how your model performs on new data.

• Train/Test Split
  • Use when: You want a quick snapshot of model performance.
  • Example: A sentiment model built on a large review dataset.
  • Risk: Results can vary wildly depending on how data was split.
• K-Fold Cross-Validation
  • Use when: You want stable performance estimates, especially on smaller datasets
  • Example: Testing a credit scoring model on 1,000 customer records.
  • How it works: Splits data into ‘k’ parts, trains on ‘k-1’, and tests on the rest—rotating until all parts are tested
  • Tool: cross_val_score, KFold in Scikit-Learn
• Stratified K-Fold
  • Use when: Working with imbalanced datasets (e.g., 90% non-fraud, 10% fraud).
  • Why: Ensures each fold has the same class ratio, preventing biased validation.
• Leave-One-Out (LOO)
  • Use when: Dataset is tiny and every data point counts.
  • Example: Clinical trials with very limited samples.
  • Trade-off: Very accurate, but computationally expensive.

3. Performance Tuning: Make a Good Model Great

After evaluating the model, tuning and optimization help you push it further—refining how it learns and improving results without changing your data source.

• Hyperparameter Tuning
  • What it does: Adjusts algorithm settings to improve learning.
  • Example: Changing max_depth in a decision tree or learning rate in a neural net.
  • Techniques:
    • Grid Search (exhaustive, slower)
    • Random Search (faster for large spaces)
    • Bayesian Optimization (smart, iterative)
  • Tools: GridSearchCV, Optuna, Hyperopt
• Feature Engineering
  • What it does: Creates new variables that expose better patterns.
  • Example: Instead of using "last login," use "days since last login."
  • Tip: Domain knowledge often drives strong features.
  • Tools: pandas, FeatureTools
• Ensemble Learning
  • What it does: Combines multiple models for better accuracy and robustness.
  • Examples:
    • Random Forest: Many decision trees, reduces variance
    • XGBoost / LightGBM: Sequential boosting for higher accuracy
    • Stacking: Combine different models’ predictions (e.g., logistic + tree + SVM)
  • Why it works: Aggregates strengths of weak models and reduces individual model bias

Pro Tip: Track all your experiments—metrics, parameters, and outputs—using tools like MLflow, Weights & Biases, or TensorBoard. You’ll move faster and avoid repeating work.

Also Read: Data Mining Techniques & Tools: Types of Data, Methods, Applications [With Examples]

Once your model is evaluated and tuned, the next step is turning it into something the business can actually use.

How Can You Deploy a Data Mining Model in Real-World Applications?

Even the most accurate model is useless if it stays in a notebook. Deployment is the process of moving your model from a development sandbox into a real-world system. Whether you're enabling product recommendations, scoring loan applications, or flagging fraudulent activity, deployment makes your model operational and accessible.

There’s no one-size-fits-all approach. Your deployment strategy depends on how the model will be used, how fast predictions are needed, and how well it needs to scale. Here’s a quick breakdown of common deployment strategies:

Key Components of Model Deployment

1. Converting the Model into a Deployable API

Transform your trained model into a production-ready service that external systems can call to get predictions.

• Tools: Flask, FastAPI, Django REST Framework
• Process:
  • Serialize the trained model using joblib or pickle
  • Load the model in a Python API endpoint
  • Expose prediction logic via a REST API
• Example Use Case:
  A customer submits a support ticket, triggering an API call that scores their churn risk in real time.

2. Integrating with Business Systems

To deliver impact, your model must plug into existing business tools where decisions happen.

• Options:
  • CRM Integration (e.g., Salesforce) for prioritizing leads
  • ERP/BI Tools (e.g., Power BI, Tableau) for predictive dashboards
    Web/Mobile Apps for real-time personalization (e.g., product recommendations)
• Key Considerations:
  • Data flow and format compatibility
  • Latency and request limits
  • Authentication, logging, and API versioning

3. Monitoring & Maintenance

Deployment isn’t set-and-forget. Models degrade over time due to changing data, known as data drift.

• Why It Matters:
  A model that performed well last month might misfire today if user behavior, market conditions, or input features shift.
• What to Monitor:
  • Prediction accuracy and output distributions
  • Latency and response time
  • Frequency of input anomalies (e.g., missing or unusual values)
• Tools: Prometheus, Grafana (system metrics); MLflow, Seldon, AWS SageMaker Model Monitor (model-specific tracking)

Also Read: 25+ Real-World Data Mining Examples That Are Transforming Industries

To deploy a model effectively, you need the right tools—here’s what powers modern data mining workflows behind the scenes.

Tools & Technologies Used in Data Mining Models

Building and deploying a data mining model requires more than just algorithms—you need the right programming languages, machine learning libraries, data platforms, and visualization tools to move efficiently from raw data to real-world insights.

Below is a breakdown of widely used tools and technologies, what they’re good at, and when to use them:

Choosing the Right Toolset: What to Consider

• Interoperability matters: Many tools work best when combined—Python with SQL for preprocessing, or TensorFlow models visualized in Power BI. Design your stack to communicate smoothly across environments.
• Think beyond prototyping: Tools like Scikit-learn are great for quick testing, but consider how well your code can scale or integrate into a production pipeline down the line.
• Community support can save time: Open-source tools like Python and Pandas have massive user communities and pre-built solutions for common problems—reducing your development effort.
  Cloud platforms offer flexibility—but at a cost: Use them strategically. For example, train locally and deploy in the cloud only when scale or uptime is critical.
• Visualization isn’t just for presentation: Strong visuals help validate assumptions early in the data mining process and catch issues before they affect model outcomes.
• Security and compliance are often overlooked: If you're working with sensitive data (e.g., healthcare or finance), prioritize tools that support encryption, audit logs, and access control out of the box.

Also Read: Exploring the Impact of Data Mining Applications Across Multiple Industries

With the right tools in place, the next step is understanding the real-world challenges of building, maintaining, and scaling data mining models.

Challenges & Best Practices in Building and Maintaining Data Mining Models

A model trained on outdated data or built without accounting for data imbalance can fail, no matter how sophisticated the algorithm. From biased inputs to inefficient pipelines, data mining often runs into avoidable issues that affect performance, reliability, and business impact. To build models that are scalable, trusted, and consistently accurate, it’s important to recognize these challenges early and apply the right practices to address them.

Understanding where things commonly break down is the first step to building models that actually deliver value.

Best Practices for Building Robust Models

Avoiding mistakes isn’t enough. You also need to design workflows that build consistency, transparency, and long-term value into your models.

Before writing code, build structure into your process. These practices help ensure your models are efficient, reliable, and ready to scale:

• Use Strong Preprocessing Pipelines:
  Automate cleaning, encoding, and normalization using tools like scikit-learn pipelines. This keeps preprocessing consistent across training and deployment environments.
• Apply Proper Validation Strategies:
  K-fold and stratified cross-validation prevent misleading performance estimates and expose model weaknesses before they reach production.
• Monitor for Data Drift:
  Use tools like Evidently AI to track changes in input features over time. Subtle shifts in user behavior or system logs can quietly degrade performance.
• Optimize for Simplicity First:
  Start with linear models or shallow trees. If they work well, they’re easier to explain, debug, and put into production than black-box alternatives.
• Benchmark Against Baselines:
  Use dummy classifiers, last-known-values, or rule-based models as references. If your model can’t beat simple logic, it may not be adding value.

As models increasingly inform real-world decisions, ethical responsibility must be part of your workflow.

Ethical Considerations in Data Mining

Building responsible models isn’t just about accuracy—it’s about accountability, fairness, and transparency.

• Bias Mitigation:
  Audit feature distributions by demographic segments. Apply re-sampling or re-weighting to reduce imbalances. Use fairness libraries to test different definitions of fairness.
• Data Privacy:
  When working with sensitive data, minimize risk with techniques like data masking, tokenization, and differential privacy. Always follow jurisdictional laws like GDPR and HIPAA.
• Responsible AI Practices:
  Incorporate model cards, document model behavior under edge cases, and avoid opaque systems in high-stakes use cases. Embed human review where outcomes affect people directly.

If you're looking to build data mining models that hold up in real-world environments, the right training can make all the difference.

How Can upGrad Help You Build Data Mining Models?

upGrad’s programs are designed to equip you with the tools to build a data model from the ground up, covering everything from data architecture and predictive modeling to analytics workflows and deployment.

With 10M+ learners, 200+ programs, and 1,400+ hiring partners, upGrad delivers hands-on, outcome-focused learning with real-world projects and guidance from industry experts.

Here are some of upGrad’s advanced programs that focus on building data mining models for applications in analytics, finance, and large-scale systems:

• Master’s Degree in Artificial Intelligence and Data Science
• Masters in Data Science Degree
• Post Graduate Certificate in Data Science & AI (Executive)

For faster upskilling in key areas like market intelligence, quantitative analysis, and data modeling, upGrad also offers specialized certifications and diplomas:

• Executive Diploma in Data Science & AI with IIIT-B
• Professional Certificate Program in AI and Data Science

Not sure which data role or learning path fits your goals best?  Get personalized career counseling to identify the best opportunities for you. Visit upGrad’s offline centers for expert mentorship, hands-on workshops, and networking sessions to connect you with industry leaders!

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Reference:

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