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Linear Regression: Predicts a dependent variable’s value based on one or more independent variables.
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Clustering: Groups similar data points together, uncovering patterns within the data.
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Decision Tree: Tree-like model that makes decisions based on input features.
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Neural Networks: Mimics the human brain’s structure to recognize patterns.
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Reinforcement Learning: Teaches models to make sequences of decisions to maximize a reward.
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Logistic Regression: Used for binary classification problems, estimating the probability of an event.
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Naive Bayes: Classifies data based on Bayes’ Theorem, assuming independence between features.
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Supervised Learning: Model is trained on a labeled dataset to make predictions.
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Support Vector Machine: Classifies data points by finding the optimal hyperplane.
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Probability: Measures the likelihood of an event occurring.
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Random Forest: Ensemble learning method using multiple decision trees.
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Variance: Measures how spread out a set of values are.
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Evaluation Metrics: Criteria to assess the performance of a model.
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Bagging: Technique combining predictions from multiple models to improve accuracy.
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Data Wrangling: Preprocessing and cleaning of raw data.
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Dimensionality Reduction: Reduces the number of input variables in a dataset.
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K-nearest Neighbors Algorithm: Classifies data points based on the majority class of their neighbors.
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Programming: Writing code for data analysis and modeling.
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Regularization: Prevents overfitting by adding a penalty term to the model.
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Statistics: Analyzing and interpreting data to extract meaningful insights.
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Binomial Distribution: Describes the number of successes in a fixed number of trials.
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Bootstrap Sampling: Resampling technique to estimate the sampling distribution.
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Exploratory Data Analysis: Analyzing data sets to summarize their main characteristics.
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Data Collection: Gathering information for analysis and modeling.