model-evaluation-and-tuning

There are 0 repository under model-evaluation-and-tuning topic.

venkat-a / Capstone-Project-IBM
Analyzing housing complaints in New-York-City and develop a forecasting model
data-engineering descriptive-statistics geographical-analysis numpy-arrays pandas-dataframe statical-analysis time-series-forecasting typecasting model-evaluation-and-tuning
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venkat-a / GoodBelly-Marketing-Analytics-Project
Marketing strategies on the sales volume and average retail price (ARP) of Good Belly products. Using a dataset encompassing sales, promotions, and demographic information across multiple regions, this project employs causal analysis and multiple linear regression to provide insights into the effectiveness of marketing activities.
casual-inference descriptive-statistics exploratory-data-analysis managerial-economics rprogramming statistical-analysis logistic-regression metric-analysis model-evaluation-and-tuning
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venkat-a / Happiness-Prediction
Prediction of happy Customers based on Happiness Survey Data
data-exploration-and-preprocessing hyperparameter-tuning machine-learning metrics sklearn visualization matplotlib seaborn model-evaluation-and-tuning
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venkat-a / Term-Deposit-Marketing-Prediction
client subsection to a term deposit
data-processing feature-engineering hyperparameter-tuning-in-pipeline lime machine-learning matplotlib probability-threshold-optimization seaborn shap sklearn statical-analysis visualization model-evaluation-and-tuning one-hot-encoding
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venkat-a / Text_Processing_RNN_LSTM
Text Processing RNN leverages RNN and LSTM models for advanced text processing. It features deep learning techniques for NLP tasks, utilizing GloVe for word embeddings, aimed at both educational and practical applications.
model-evaluation-and-tuning categorical-encoding deep-neural-networks embedding-vectors lstm-neural-networks nltk-tokenizer numpy-arrays pandas-dataframe pytorch-nlp rnn-encoder-decoder tokenization vocabulary-builder
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venkat-a / Unveiling-Patterns-in-Traffic-Crashes-A-Data-Driven-Approach-to-Safer-Roads
By integrating geographical data analysis and statistical modeling, CTCA aims to inform strategies for reducing crash rates and enhancing road safety. This initiative combines innovative data processing techniques with advanced analytics to offer actionable recommendations for policymakers, urban planners, and public safety organizations.
exploratory-data-analysis tableau feature-engineering hypothesis-generation numpy-arrays pandas-dataframe visualization matplotlib seaborn feature-extraction model-evaluation-and-tuning
Language:Jupyter Notebook