How to convert image to pdf in Python
# Python3 program to convert image to pfd
# using img2pdf library
# importing necessary libraries
import img2pdf
from PIL import Image
import os
# storing image path
img_path = "Input.png"
# storing pdf path
pdf_path = "file_pdf.pdf"
# opening image
image = Image.open(img_path)
# converting into chunks using img2pdf
pdf_bytes = img2pdf.convert(image.filename)
# opening or creating pdf file
file = open(pdf_path, "wb")
# writing pdf files with chunks
file.write(pdf_bytes)
# closing image file
image.close()
# closing pdf file
file.close()
# output
print("Successfully made pdf file")
pip3 install pillow && pip3 install img2pdf👍12❤1
⭐⭐⭐ Advance Level Data science Projects ⭐⭐⭐
1) Identify your Digits Dataset : https://www.kaggle.com/c/digit-recognizer/data
2) Recommendation Engine : https://cseweb.ucsd.edu/~jmcauley/datasets.html
3) Visual QA : https://visualqa.org/download.html
4) Vox Celebrity : https://www.robots.ox.ac.uk/~vgg/data/voxceleb/
5) Breast cancer classification : https://www.kaggle.com/martinab/breast-cancer-classification-wisconsin-dataset
6) Traffic signals : https://benchmark.ini.rub.de/?section=gtsrb&subsection=dataset
7) Image caption generator : https://academictorrents.com/details/9dea07ba660a722ae1008c4c8afdd303b6f6e53b
1) Identify your Digits Dataset : https://www.kaggle.com/c/digit-recognizer/data
2) Recommendation Engine : https://cseweb.ucsd.edu/~jmcauley/datasets.html
3) Visual QA : https://visualqa.org/download.html
4) Vox Celebrity : https://www.robots.ox.ac.uk/~vgg/data/voxceleb/
5) Breast cancer classification : https://www.kaggle.com/martinab/breast-cancer-classification-wisconsin-dataset
6) Traffic signals : https://benchmark.ini.rub.de/?section=gtsrb&subsection=dataset
7) Image caption generator : https://academictorrents.com/details/9dea07ba660a722ae1008c4c8afdd303b6f6e53b
👍8
Which of the following evaluation metrics may be used in classification?
Anonymous Poll
58%
F1 score
11%
Log loss
6%
Jaccard index
26%
All of the above
🔥2
6 Data Science Projects for your portfolio
1. Predictive Analytics Project
Build a model to predict future outcomes based on historical data.
Skills Demonstrated: ML, data preprocessing, feature engineering, model evaluation.
2. Time Series Analysis Project
Analyze time series data to identify trends, seasonal patterns, and anomalies. You could work on projects like stock market analysis.
Skills Demonstrated: Time series decomposition, forecasting models, data preprocessing
3. Recommender System
Develop a recommendation engine for products, articles, songs any other items. You can use collaborative filtering, content-based filtering, or hybrid methods.
Skills Demonstrated: Recommendation algorithms, data preprocessing, model evaluation.
4. Customer Segmentation Project
Use clustering algorithms to segment customers based on their behavior and characteristics. This could involve dividing customers into groups for targeted marketing.
Skills Demonstrated: Clustering algorithms (K-means, DBSCAN), data preprocessing, feature selection.
5. Anomaly Detection Project
Develop a model to detect anomalies in data, such as fraud detection in financial transactions.
Skills Demonstrated: Anomaly detection techniques, data preprocessing, model evaluation.
6. Churn Prediction for Subscription Services
Predict which customers are likely to cancel their subscriptions based on their usage patterns and other factors.
Skills Demonstrated: Machine learning, data preprocessing, feature engineering, model evaluation.
Join for more: https://t.iss.one/pythonspecialist
1. Predictive Analytics Project
Build a model to predict future outcomes based on historical data.
Skills Demonstrated: ML, data preprocessing, feature engineering, model evaluation.
2. Time Series Analysis Project
Analyze time series data to identify trends, seasonal patterns, and anomalies. You could work on projects like stock market analysis.
Skills Demonstrated: Time series decomposition, forecasting models, data preprocessing
3. Recommender System
Develop a recommendation engine for products, articles, songs any other items. You can use collaborative filtering, content-based filtering, or hybrid methods.
Skills Demonstrated: Recommendation algorithms, data preprocessing, model evaluation.
4. Customer Segmentation Project
Use clustering algorithms to segment customers based on their behavior and characteristics. This could involve dividing customers into groups for targeted marketing.
Skills Demonstrated: Clustering algorithms (K-means, DBSCAN), data preprocessing, feature selection.
5. Anomaly Detection Project
Develop a model to detect anomalies in data, such as fraud detection in financial transactions.
Skills Demonstrated: Anomaly detection techniques, data preprocessing, model evaluation.
6. Churn Prediction for Subscription Services
Predict which customers are likely to cancel their subscriptions based on their usage patterns and other factors.
Skills Demonstrated: Machine learning, data preprocessing, feature engineering, model evaluation.
Join for more: https://t.iss.one/pythonspecialist
Telegram
Data Science Projects
Perfect channel for Data Scientists
Learn Python, AI, R, Machine Learning, Data Science and many more
Admin: @love_data
Learn Python, AI, R, Machine Learning, Data Science and many more
Admin: @love_data
👍16
I am starting with a data science interview series to check your knowledge, let's start with the first question. Here it is:
Question 1:
Explain the difference between supervised and unsupervised learning.
Let me know answer in comments 👇👇
Question 1:
Explain the difference between supervised and unsupervised learning.
Let me know answer in comments 👇👇
👍23❤5
Which of the following is a cluster computing framework that specialises in working with big data?
Anonymous Poll
8%
HTML
51%
Apache Spark
4%
CSS
28%
Pandas
9%
Scipy
👎4👍2❤1👏1
Question 2:
What is overfitting in machine learning, and how can you prevent it?
What is overfitting in machine learning, and how can you prevent it?
👍19❤2🔥1
Question 3:
What is the bias-variance tradeoff in machine learning?
What is the bias-variance tradeoff in machine learning?
👍9❤1
Question 4:
What are some common techniques to handle missing data in a dataset?
What are some common techniques to handle missing data in a dataset?
👍6
5 DataAnalytics Project Ideas to boost your resume:
1. Stock Market Portfolio Optimization
2. YouTube Data Collection & Analysis
3. Elections Ad Spending & Voting Patterns Analysis
4. EV Market Size Analysis
5. Metro Operations Optimization
1. Stock Market Portfolio Optimization
2. YouTube Data Collection & Analysis
3. Elections Ad Spending & Voting Patterns Analysis
4. EV Market Size Analysis
5. Metro Operations Optimization
👏29👍5❤4
Question 5:
Explain the concept of a confusion matrix. What are precision, recall, and F1-score?
Explain the concept of a confusion matrix. What are precision, recall, and F1-score?
Question 6:
What is cross-validation, and why is it important in machine learning?
What is cross-validation, and why is it important in machine learning?
👍8