Coding Projects in Python (DK).pdf
21.9 MB
Coding projects in Python
DK, 2017
DK, 2017
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15 Best Project Ideas for Backend Development : 🛠️🌐
🚀 Beginner Level :
1. 📦 RESTful API for a To-Do App
2. 📝 Contact Form Backend
3. 🗂️ File Upload Service
4. 📬 Email Subscription Service
5. 🧾 Notes App Backend
🌟 Intermediate Level :
6. 🛒 E-commerce Backend with Cart & Orders
7. 🔐 Authentication System (JWT/OAuth)
8. 🧑🤝🧑 User Management API
9. 🧾 Invoice Generator API
10. 🧠 Blog CMS Backend
🌌 Advanced Level :
11. 🧠 AI Chatbot Backend Integration
12. 📈 Real-Time Stock Tracker using WebSockets
13. 🎧 Music Streaming Server
14. 💬 Real-Time Chat Server
15. ⚙️ Microservices Architecture for Large Apps
Here you can find more Coding Project Ideas: https://whatsapp.com/channel/0029VazkxJ62UPB7OQhBE502
Web Development Jobs: https://whatsapp.com/channel/0029Vb1raTiDjiOias5ARu2p
JavaScript Resources: https://whatsapp.com/channel/0029VavR9OxLtOjJTXrZNi32
ENJOY LEARNING 👍👍
🚀 Beginner Level :
1. 📦 RESTful API for a To-Do App
2. 📝 Contact Form Backend
3. 🗂️ File Upload Service
4. 📬 Email Subscription Service
5. 🧾 Notes App Backend
🌟 Intermediate Level :
6. 🛒 E-commerce Backend with Cart & Orders
7. 🔐 Authentication System (JWT/OAuth)
8. 🧑🤝🧑 User Management API
9. 🧾 Invoice Generator API
10. 🧠 Blog CMS Backend
🌌 Advanced Level :
11. 🧠 AI Chatbot Backend Integration
12. 📈 Real-Time Stock Tracker using WebSockets
13. 🎧 Music Streaming Server
14. 💬 Real-Time Chat Server
15. ⚙️ Microservices Architecture for Large Apps
Here you can find more Coding Project Ideas: https://whatsapp.com/channel/0029VazkxJ62UPB7OQhBE502
Web Development Jobs: https://whatsapp.com/channel/0029Vb1raTiDjiOias5ARu2p
JavaScript Resources: https://whatsapp.com/channel/0029VavR9OxLtOjJTXrZNi32
ENJOY LEARNING 👍👍
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📈 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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Difference between list and tuple in python
🔸List is mutable ( you can modify the original list) and it's values are written in sqare brackets [ ]
🔸Tuple is immutable ( you can't modify it) and it's values are written in parentheses ( ) delimited by comma( , )
🔸To convert list to tuple - we use tuple() function
list1 = [1,2,3]
print(tuple(list1)) Output : (1,2,3)
🔸 For single element list
list1 = [1]
print(tuple(list1)) Output : (1, )
▪️a tuple is a tuple because of comma not because of parentheses
🔸List is mutable ( you can modify the original list) and it's values are written in sqare brackets [ ]
🔸Tuple is immutable ( you can't modify it) and it's values are written in parentheses ( ) delimited by comma( , )
🔸To convert list to tuple - we use tuple() function
list1 = [1,2,3]
print(tuple(list1)) Output : (1,2,3)
🔸 For single element list
list1 = [1]
print(tuple(list1)) Output : (1, )
▪️a tuple is a tuple because of comma not because of parentheses
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Here are some essential data science concepts from A to Z:
A - Algorithm: A set of rules or instructions used to solve a problem or perform a task in data science.
B - Big Data: Large and complex datasets that cannot be easily processed using traditional data processing applications.
C - Clustering: A technique used to group similar data points together based on certain characteristics.
D - Data Cleaning: The process of identifying and correcting errors or inconsistencies in a dataset.
E - Exploratory Data Analysis (EDA): The process of analyzing and visualizing data to understand its underlying patterns and relationships.
F - Feature Engineering: The process of creating new features or variables from existing data to improve model performance.
G - Gradient Descent: An optimization algorithm used to minimize the error of a model by adjusting its parameters.
H - Hypothesis Testing: A statistical technique used to test the validity of a hypothesis or claim based on sample data.
I - Imputation: The process of filling in missing values in a dataset using statistical methods.
J - Joint Probability: The probability of two or more events occurring together.
K - K-Means Clustering: A popular clustering algorithm that partitions data into K clusters based on similarity.
L - Linear Regression: A statistical method used to model the relationship between a dependent variable and one or more independent variables.
M - Machine Learning: A subset of artificial intelligence that uses algorithms to learn patterns and make predictions from data.
N - Normal Distribution: A symmetrical bell-shaped distribution that is commonly used in statistical analysis.
O - Outlier Detection: The process of identifying and removing data points that are significantly different from the rest of the dataset.
P - Precision and Recall: Evaluation metrics used to assess the performance of classification models.
Q - Quantitative Analysis: The process of analyzing numerical data to draw conclusions and make decisions.
R - Random Forest: An ensemble learning algorithm that builds multiple decision trees to improve prediction accuracy.
S - Support Vector Machine (SVM): A supervised learning algorithm used for classification and regression tasks.
T - Time Series Analysis: A statistical technique used to analyze and forecast time-dependent data.
U - Unsupervised Learning: A type of machine learning where the model learns patterns and relationships in data without labeled outputs.
V - Validation Set: A subset of data used to evaluate the performance of a model during training.
W - Web Scraping: The process of extracting data from websites for analysis and visualization.
X - XGBoost: An optimized gradient boosting algorithm that is widely used in machine learning competitions.
Y - Yield Curve Analysis: The study of the relationship between interest rates and the maturity of fixed-income securities.
Z - Z-Score: A standardized score that represents the number of standard deviations a data point is from the mean.
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A - Algorithm: A set of rules or instructions used to solve a problem or perform a task in data science.
B - Big Data: Large and complex datasets that cannot be easily processed using traditional data processing applications.
C - Clustering: A technique used to group similar data points together based on certain characteristics.
D - Data Cleaning: The process of identifying and correcting errors or inconsistencies in a dataset.
E - Exploratory Data Analysis (EDA): The process of analyzing and visualizing data to understand its underlying patterns and relationships.
F - Feature Engineering: The process of creating new features or variables from existing data to improve model performance.
G - Gradient Descent: An optimization algorithm used to minimize the error of a model by adjusting its parameters.
H - Hypothesis Testing: A statistical technique used to test the validity of a hypothesis or claim based on sample data.
I - Imputation: The process of filling in missing values in a dataset using statistical methods.
J - Joint Probability: The probability of two or more events occurring together.
K - K-Means Clustering: A popular clustering algorithm that partitions data into K clusters based on similarity.
L - Linear Regression: A statistical method used to model the relationship between a dependent variable and one or more independent variables.
M - Machine Learning: A subset of artificial intelligence that uses algorithms to learn patterns and make predictions from data.
N - Normal Distribution: A symmetrical bell-shaped distribution that is commonly used in statistical analysis.
O - Outlier Detection: The process of identifying and removing data points that are significantly different from the rest of the dataset.
P - Precision and Recall: Evaluation metrics used to assess the performance of classification models.
Q - Quantitative Analysis: The process of analyzing numerical data to draw conclusions and make decisions.
R - Random Forest: An ensemble learning algorithm that builds multiple decision trees to improve prediction accuracy.
S - Support Vector Machine (SVM): A supervised learning algorithm used for classification and regression tasks.
T - Time Series Analysis: A statistical technique used to analyze and forecast time-dependent data.
U - Unsupervised Learning: A type of machine learning where the model learns patterns and relationships in data without labeled outputs.
V - Validation Set: A subset of data used to evaluate the performance of a model during training.
W - Web Scraping: The process of extracting data from websites for analysis and visualization.
X - XGBoost: An optimized gradient boosting algorithm that is widely used in machine learning competitions.
Y - Yield Curve Analysis: The study of the relationship between interest rates and the maturity of fixed-income securities.
Z - Z-Score: A standardized score that represents the number of standard deviations a data point is from the mean.
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Like if you need similar content 😄👍
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