Forwarded from Python for Data Analysts
📈 Predictive Modeling for Future Stock Prices in Python: A Step-by-Step Guide
The process of building a stock price prediction model using Python.
1. Import required modules
2. Obtaining historical data on stock prices
3. Selection of features.
4. Definition of features and target variable
5. Preparing data for training
6. Separation of data into training and test sets
7. Building and training the model
8. Making forecasts
9. Trading Strategy Testing
The process of building a stock price prediction model using Python.
1. Import required modules
2. Obtaining historical data on stock prices
3. Selection of features.
4. Definition of features and target variable
5. Preparing data for training
6. Separation of data into training and test sets
7. Building and training the model
8. Making forecasts
9. Trading Strategy Testing
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Data Science Bookcamp Five real-world Python projects.pdf
42.4 MB
Data Science Bookcamp
Leonard Apeltsin, 2021
Leonard Apeltsin, 2021
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Forwarded from Programming Resources | Python | Javascript | Artificial Intelligence Updates | Computer Science Courses | AI Books
Cloud Comparison Cheat Sheet
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🚨30 FREE Dataset Sources for Data Science Projects🔥
Data Simplifier: https://datasimplifier.com/best-data-analyst-projects-for-freshers/
US Government Dataset: https://www.data.gov/
Open Government Data (OGD) Platform India: https://data.gov.in/
The World Bank Open Data: https://data.worldbank.org/
Data World: https://data.world/
BFI - Industry Data and Insights: https://www.bfi.org.uk/data-statistics
The Humanitarian Data Exchange (HDX): https://data.humdata.org/
Data at World Health Organization (WHO): https://www.who.int/data
FBI’s Crime Data Explorer: https://crime-data-explorer.fr.cloud.gov/
AWS Open Data Registry: https://registry.opendata.aws/
FiveThirtyEight: https://data.fivethirtyeight.com/
IMDb Datasets: https://www.imdb.com/interfaces/
Kaggle: https://www.kaggle.com/datasets
UCI Machine Learning Repository: https://archive.ics.uci.edu/ml/index.php
Google Dataset Search: https://datasetsearch.research.google.com/
Nasdaq Data Link: https://data.nasdaq.com/
Recommender Systems and Personalization Datasets: https://cseweb.ucsd.edu/~jmcauley/datasets.html
Reddit - Datasets: https://www.reddit.com/r/datasets/
Open Data Network by Socrata: https://www.opendatanetwork.com/
Climate Data Online by NOAA: https://www.ncdc.noaa.gov/cdo-web/
Azure Open Datasets: https://azure.microsoft.com/en-us/services/open-datasets/
IEEE Data Port: https://ieee-dataport.org/
Wikipedia: Database: https://dumps.wikimedia.org/
BuzzFeed News: https://github.com/BuzzFeedNews/everything
Academic Torrents: https://academictorrents.com/
Yelp Open Dataset: https://www.yelp.com/dataset
The NLP Index by Quantum Stat: https://index.quantumstat.com/
Computer Vision Online: https://www.computervisiononline.com/dataset
Visual Data Discovery: https://www.visualdata.io/
Roboflow Public Datasets: https://public.roboflow.com/
Computer Vision Group, TUM: https://vision.in.tum.de/data/datasets
Data Simplifier: https://datasimplifier.com/best-data-analyst-projects-for-freshers/
US Government Dataset: https://www.data.gov/
Open Government Data (OGD) Platform India: https://data.gov.in/
The World Bank Open Data: https://data.worldbank.org/
Data World: https://data.world/
BFI - Industry Data and Insights: https://www.bfi.org.uk/data-statistics
The Humanitarian Data Exchange (HDX): https://data.humdata.org/
Data at World Health Organization (WHO): https://www.who.int/data
FBI’s Crime Data Explorer: https://crime-data-explorer.fr.cloud.gov/
AWS Open Data Registry: https://registry.opendata.aws/
FiveThirtyEight: https://data.fivethirtyeight.com/
IMDb Datasets: https://www.imdb.com/interfaces/
Kaggle: https://www.kaggle.com/datasets
UCI Machine Learning Repository: https://archive.ics.uci.edu/ml/index.php
Google Dataset Search: https://datasetsearch.research.google.com/
Nasdaq Data Link: https://data.nasdaq.com/
Recommender Systems and Personalization Datasets: https://cseweb.ucsd.edu/~jmcauley/datasets.html
Reddit - Datasets: https://www.reddit.com/r/datasets/
Open Data Network by Socrata: https://www.opendatanetwork.com/
Climate Data Online by NOAA: https://www.ncdc.noaa.gov/cdo-web/
Azure Open Datasets: https://azure.microsoft.com/en-us/services/open-datasets/
IEEE Data Port: https://ieee-dataport.org/
Wikipedia: Database: https://dumps.wikimedia.org/
BuzzFeed News: https://github.com/BuzzFeedNews/everything
Academic Torrents: https://academictorrents.com/
Yelp Open Dataset: https://www.yelp.com/dataset
The NLP Index by Quantum Stat: https://index.quantumstat.com/
Computer Vision Online: https://www.computervisiononline.com/dataset
Visual Data Discovery: https://www.visualdata.io/
Roboflow Public Datasets: https://public.roboflow.com/
Computer Vision Group, TUM: https://vision.in.tum.de/data/datasets
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sergio-j-rojas-g-learning-scipy-for-numerical-and-2015.pdf
3.5 MB
Learning SciPy for Numerical and Scientific Computing
Sergio J. Rojas G., 2015
Sergio J. Rojas G., 2015
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Marketing Research with R and Python.pdf
22.7 MB
Marketing Research with R and Python
Howard Pong Yuen Lam, 2023
Howard Pong Yuen Lam, 2023
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1680810253047.docx
54.8 KB
One of the most effective ways to learn machine learning is by getting hands-on experience and building something yourself.
While finding inspiration can be challenging, exploring projects by others can open your eyes to the endless possibilities. 💡
The projects I am sharing are perfect for those new to machine learning and curious about its potential.
While finding inspiration can be challenging, exploring projects by others can open your eyes to the endless possibilities. 💡
The projects I am sharing are perfect for those new to machine learning and curious about its potential.
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20 Python Libraries You Aren't Using (But Should).pdf
4.1 MB
20 Python Libraries You
Aren’t Using (But Should)
Caleb Hattingh, 2016
Aren’t Using (But Should)
Caleb Hattingh, 2016
Advice from 25 Amazing Data Scientist.pdf
2.8 MB
Resource Pdf :- Advice from 25 Amazing Data Scientists.
Source :- Jake Klamka
Source :- Jake Klamka
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