%pip install openai
%pip install catboost

import openai
openai.api_key = "your-key"

completion = openai.Completion.create(engine="text-davinci-003", prompt="What is the pandas library?", max_tokens=1000)
print(completion.choices[0]['text'])
# output
Pandas is an open source software library written in Python for data manipulation and analysis. Pandas is widely used in data science, machine learning and many other fields. It provides high-level data structures and tools for handling and manipulating data, including data frames, series, plotting tools and more.

completion = openai.Completion.create(engine="text-davinci-003", prompt="what are some common Pandas use cases?", max_tokens=240)
print(completion.choices[0]['text'])
# output
1. Data Cleaning and Transformation
2. Data Analysis and Exploration
3. Time Series Analysis
4. Data Visualization
5. Statistical Modeling
6. Predictive Modeling
7. Machine Learning
8. Web Scraping

#what are the most common deep learning libraries?
completion = openai.Completion.create(engine="text-davinci-003", prompt="what are the most common deep learning libraries?", max_tokens=240)
print(completion.choices[0]['text'])
# output
. TensorFlow 
2. PyTorch 
3. Keras 
4. Caffe 
5. CNTK 
6. MXNet 
7. Theano 
8. Deeplearning4j 
9. Gensim 
10. LUNA

completion = openai.Completion.create(engine="text-davinci-003", prompt="What is a deep neural network?", max_tokens=1000)
print(completion.choices[0]['text'])
# output
A deep neural network (DNN) is a type of artificial neural network (ANN) with multiple layers of neurons between the input and output layers. DNNs are designed to learn complex non-linear relationships from data, and have been successfully applied in a wide range of areas such as image recognition, natural language processing, and financial forecasting.

completion = openai.Completion.create(engine="text-davinci-003", prompt="give some ideas on feature transformations that can improve model performance", max_tokens=1000)
print(completion.choices[0]['text'])
# output
1. Standardization/Normalization: A common feature transform used to ensure features are on the same scale, standardizing or normalizing variables can help limit the severity of outliers and improve the overall model performance.
2. Feature Binning: Binning is a process of transforming numerical variables into categorical ones. This can be useful when working with variables that have too many levels and can have a significant effect on the model performance.
3. Polynomial Expansion: When a nonlinear relationship is expected between features and the output variable, a polynomial expansion feature transformation can help improve model performance.
4. Feature Selection: Removing redundant or irrelevant features from the dataset can help improve the model performance as these features may lead to overfitting.
5. Ensemble: Combining different types of models (or different versions of the same model) can often improve performance due to their combined capabilities.

completion = openai.Completion.create(engine="text-davinci-003", prompt="Write example python code that performs data standardization", max_tokens=1000)
print(completion.choices[0]['text'])
#output
# Import the necessary libraries
import numpy as np
# Define the data 
data = np.array([[-3, 9, 0, 8],
                 [ 4, 6, 5, 12],
                 [20, 2, 3, 15]])
# Calculate mean and standard deviation
mean = np.mean(data, axis=0)
std = np.std(data, axis=0)
# Perform data standardization
standardized_data = (data - mean) / std
# Print the results
print(standardized_data)

# Import the necessary libraries
import numpy as np
# Define the data 
data = np.array([[-3, 9, 0, 8],
                 [ 4, 6, 5, 12],
                 [20, 2, 3, 15]])
# Calculate mean and standard deviation
mean = np.mean(data, axis=0)
std = np.std(data, axis=0)
# Perform data standardization
standardized_data = (data - mean) / std
# Print the results
print(standardized_data)
# output
[[-1.03881504  1.16247639 -1.29777137 -1.27872403]
 [-0.31164451  0.11624764  1.13554995  0.11624764]
 [ 1.35045955 -1.27872403  0.16222142  1.16247639]]

completion = openai.Completion.create(engine="text-davinci-003", prompt="Write example python code that performs data normalization on fake data", max_tokens=1000)
print(completion.choices[0]['text'])
# output
# Normalizing data will rescale features in the range [0,1]
data = [3, 7, 10, 13] # Sample data
# Calculate the maximum and minimum of the data
max_data = max(data)
min_data = min(data)
# Normalize the data
normalized_data = [(x-min_data)/(max_data-min_data) for x in data]
# Print first value to check 
print(normalized_data[0]) # Prints 0.2

# Normalizing data will rescale features in the range [0,1]
data = [3, 7, 10, 13] # Sample data
# Calculate the maximum and minimum of the data
max_data = max(data)
min_data = min(data)
# Normalize the data
normalized_data = [(x-min_data)/(max_data-min_data) for x in data]
# Print first value to check 
print(normalized_data) 

completion = openai.Completion.create(engine="text-davinci-003", prompt="How do i select a time series model?", max_tokens=1000)
print(completion.choices[0]['text'])
# output
The selection of a time series model depends on the type of data that is being analyzed. If there are long-term trends or cycles in the data, then an autoregressive integrated moving average (ARIMA) model or a exponential smoothing (ETS) model may be the best option. If the data are seasonal or there are outliers present, then a Holt-Winters model may be a better choice. If the data do not contain any long-term trends or seasonality, a Box-Jenkins model may be suitable. Regardless of the model selected, it is important to consider the type of data that is being modeled when selecting a time series model.

completion = openai.Completion.create(engine="text-davinci-003", prompt="When to use ARIMA vs FB prophet?", max_tokens=1000)
print(completion.choices[0]['text'])
# output
ARIMA and FB Prophet are both used for time series forecasting. ARIMA is a linear model and is better for predicting long-term trends with stationary data. FB Prophet is a non-linear model and is better for predicting short-term trends with non-stationary data. So, you would use ARIMA when you are mainly concerned with predicting long-term trends, and you would use FB Prophet when you are mainly concerned with predicting short-term trends.

completion = openai.Completion.create(engine="text-davinci-003", prompt="Write example python code that generates synthetic healthcare readmission data stored in a dataframe", max_tokens=1000)
print(completion.choices[0]['text'])
# output
import pandas as pd
import numpy as np
# Create Dataframe 
df = pd.DataFrame(columns=['Patient_ID', 'Age', 'Admission_Type', 'Readmitted'])
# Generate Data
np.random.seed(0)
for i in range(10):
  admission_type = np.random.choice(['Urgent', 'Scheduled', 'Emergency'])
  patient_age = np.random.randint(18, 80)
  readmission = np.random.choice([0, 1])
  df.loc[i] = [i+1, patient_age, admission_type, readmission]
# Print Dataframe to Console
print(df)

import pandas as pd
import numpy as np
# Create Dataframe 
df = pd.DataFrame(columns=['Patient_ID', 'Age', 'Admission_Type', 'Readmitted'])
# Generate Data
np.random.seed(0)
for i in range(10):
  admission_type = np.random.choice(['Urgent', 'Scheduled', 'Emergency'])
  patient_age = np.random.randint(18, 80)
  readmission = np.random.choice([0, 1])
  df.loc[i] = [i+1, patient_age, admission_type, readmission]
# Print Dataframe to Console
df

completion = openai.Completion.create(engine="text-davinci-003", prompt="Write example python code that generates synthetic healthcare readmission data stored in a dataframe. From this write code that builds a catboost model that predicts readmission outcomes. Also write code to calculate and print performance", max_tokens=3000)
print(completion.choices[0]['text'])
# output
 metrics
## Generate Synthetic Healthcare Readmission Data
import pandas as pd 
import numpy as np 
# set the seed for reproducibility 
np.random.seed(1)
# create dataframe 
df = pd.DataFrame(np.random.randint(0, 100, size=(100, 10)), columns=['age','gender','length_of_stay','diagnosis','NIV','laboratory','past_hospitalizations','medications','bmi','readmission'])
# add labels to data frame 
df['age'] = np.random.randint(20, 80, size=(100))
df['gender'] = np.random.randint(1, 2, size=(100))
df['length_of_stay'] = np.random.randint(2, 14, size=(100))
df['diagnosis'] = np.random.randint(1, 5, size=(100))
df['NIV'] = np.random.randint(0, 2, size=(100))
df['laboratory'] = np.random.randint(1, 6, size=(100))
df['past_hospitalizations'] = np.random.randint(0, 10, size=(100))
df['medications'] = np.random.randint(1, 6, size=(100))
df['bmi'] = np.random.randint(18, 35, size=(100))
df['readmission'] = np.random.randint(0, 2, size=(100))
# print the dataframe 
print(df)
## Build a CatBoost Model
from catboost import CatBoostClassifier
from sklearn.metrics import confusion_matrix
# separate X and y
X = df.iloc[:, 0:9]
y = df.iloc[:, 9]
# initialize catboost classifier 
cat_clf = CatBoostClassifier(iterations=50,
                            learning_rate=0.3,
                            depth=8,
                            eval_metric='Accuracy',
                            random_seed=42)
# fit the model 
cat_clf.fit(X, y)
# predict values
y_pred = cat_clf.predict(X)
# print confusion matrix
conf_mat = confusion_matrix(y, y_pred)
print(conf_mat)
## Calculate and Print Performance Metrics
from sklearn.metrics import accuracy_score, precision_score, recall_score
# calculate performance metrics
acc = accuracy_score(y, y_pred)
precision = precision_score(y, y_pred)
recall = recall_score(y, y_pred)
# print performance metrics 
print("Accuracy: {}".format(acc))
print("Precision: {}".format(precision))
print("Recall: {}".format(recall))

## Generate Synthetic Healthcare Readmission Data
import pandas as pd 
import numpy as np 
# set the seed for reproducibility 
np.random.seed(1)
# create dataframe 
df = pd.DataFrame(np.random.randint(0, 100, size=(100, 10)), columns=['age','gender','length_of_stay','diagnosis','NIV','laboratory','past_hospitalizations','medications','bmi','readmission'])
# add labels to data frame 
df['age'] = np.random.randint(20, 80, size=(100))
df['gender'] = np.random.randint(1, 2, size=(100))
df['length_of_stay'] = np.random.randint(2, 14, size=(100))
df['diagnosis'] = np.random.randint(1, 5, size=(100))
df['NIV'] = np.random.randint(0, 2, size=(100))
df['laboratory'] = np.random.randint(1, 6, size=(100))
df['past_hospitalizations'] = np.random.randint(0, 10, size=(100))
df['medications'] = np.random.randint(1, 6, size=(100))
df['bmi'] = np.random.randint(18, 35, size=(100))
df['readmission'] = np.random.randint(0, 2, size=(100))
# print the dataframe 
print(df)
## Build a CatBoost Model
from catboost import CatBoostClassifier
from sklearn.metrics import confusion_matrix
# separate X and y
X = df.iloc[:, 0:9]
y = df.iloc[:, 9]
# initialize catboost classifier 
cat_clf = CatBoostClassifier(iterations=50,
                            learning_rate=0.3,
                            depth=8,
                            eval_metric='Accuracy',
                            random_seed=42)
# fit the model 
cat_clf.fit(X, y)
# predict values
y_pred = cat_clf.predict(X)
# print confusion matrix
conf_mat = confusion_matrix(y, y_pred)
print(conf_mat)
## Calculate and Print Performance Metrics
from sklearn.metrics import accuracy_score, precision_score, recall_score
# calculate performance metrics
acc = accuracy_score(y, y_pred)
precision = precision_score(y, y_pred)
recall = recall_score(y, y_pred)
# print performance metrics 
print("Accuracy: {}".format(acc))
print("Precision: {}".format(precision))
print("Recall: {}".format(recall))
# output
略

completion = openai.Completion.create(engine="text-davinci-003", prompt="Write example python code that generates synthetic transaction data stored in a dataframe", max_tokens=1000)
print(completion.choices[0]['text'])
# output
import pandas as pd 
import numpy as np 
#create randomly generated customer data
customer_id = np.arange(1,101) 
customer_names = [f'John Doe {x}' for x in range(1,101)] 
#create randomly generated transaction data
transaction_id = np.arange(1,101)
dates = [f'2020-07-{x}' for x in range(1,101)]
amounts = np.random.randint(low=1, high=1000, size=(100,)) 
#create dataframe with randomly generated data
transaction_data = pd.DataFrame({'Customer ID': customer_id, 
                            'Customer Name': customer_names,
                            'Transaction ID': transaction_id, 
                            'Date': dates, 
                            'Amount': amounts})
print(transaction_data)

import pandas as pd 
import numpy as np 
#create randomly generated customer data
customer_id = np.arange(1,101) 
customer_names = [f'John Doe {x}' for x in range(1,101)] 
#create randomly generated transaction data
transaction_id = np.arange(1,101)
dates = [f'2020-07-{x}' for x in range(1,101)]
amounts = np.random.randint(low=1, high=1000, size=(100,)) 
#create dataframe with randomly generated data
transaction_data = pd.DataFrame({'Customer ID': customer_id, 
                            'Customer Name': customer_names,
                            'Transaction ID': transaction_id, 
                            'Date': dates, 
                            'Amount': amounts})
transaction_data

completion = openai.Completion.create(engine="text-davinci-003", prompt="Write example python code that generates synthetic transaction data stored in a dataframe. Include customer ID, transaction amount, item ID, item name, age, gender, and zipcode", max_tokens=2000)
print(completion.choices[0]['text'])
# output
import pandas as pd
import numpy as np
rows = ['customer_ID', 'transaction_amnt', 'item_ID', 'item_name', 'age', 'gender', 'zipcode']
data = pd.DataFrame(columns=rows)  
for i in range(1,100):
        customer_ID = int( np.random.uniform(100,600-100)) 
        transaction_amnt = np.random.uniform(1.25, 10.00)
        item_ID = int( np.random.uniform(1,35))
        item_name = np.random.choice(["phone", "tablet", "laptop", "smartwatch"])
        age = int( np.random.uniform(17,75)) 
        gender = np.random.choice(["male", "female"]) 
        zipcode = np.random.choice(["98101", "98200", "98469", "98801"])
        data.loc[i] = [customer_ID, transaction_amnt, item_ID, item_name, age, gender, zipcode]
print (data)

import pandas as pd
import numpy as np
rows = ['customer_ID', 'transaction_amnt', 'item_ID', 'item_name', 'age', 'gender', 'zipcode']
data = pd.DataFrame(columns=rows)  
for i in range(1,100):
        customer_ID = int( np.random.uniform(100,600-100)) 
        transaction_amnt = np.random.uniform(1.25, 10.00)
        item_ID = int( np.random.uniform(1,35))
        item_name = np.random.choice(["phone", "tablet", "laptop", "smartwatch"])
        age = int( np.random.uniform(17,75)) 
        gender = np.random.choice(["male", "female"]) 
        zipcode = np.random.choice(["98101", "98200", "98469", "98801"])
        data.loc[i] = [customer_ID, transaction_amnt, item_ID, item_name, age, gender, zipcode]
data

completion = openai.Completion.create(engine="text-davinci-003", prompt=" list some good public datasets", max_tokens=1000)
print(completion.choices[0]['text'])
# output
1. US Census Data
2. Enron Email Dataset
3. Global Open Data Index
4. Air Quality Monitoring Data
5. New York City Taxi Trip Data
6. IMF Data
7. World Bank Open Data
8. Google Books Ngrams Dataset
9. Amazon Reviews Dataset
10. UCI Machine Learning Repository

completion = openai.Completion.create(engine="text-davinci-003", prompt=" list some good public datasets under apache 2.0 license. provide links to their source", max_tokens=1000, temperature=0)
print(completion.choices[0]['text'])
# output
1. OpenStreetMap: https://www.openstreetmap.org/
2. US Census Data: https://www.census.gov/data.html
3. Google Books Ngrams: https://aws.amazon.com/datasets/google-books-ngrams/
4. Wikipedia: https://dumps.wikimedia.org/enwiki/
5. US Government Spending Data: https://www.usaspending.gov/
6. World Bank Open Data: https://data.worldbank.org/
7. Common Crawl: http://commoncrawl.org/
8. Open Images: https://storage.googleapis.com/openimages/web/index.html
9. OpenFlights: https://openflights.org/data.html
10. GDELT: http://data.gdeltproject.org/

completion = openai.Completion.create(engine="text-davinci-003", prompt=" list some good public datasets under apache 2.0 license. provide links to their source and descriptions", max_tokens=1000, temperature=0)
print(completion.choices[0]['text'])
# output
1. OpenStreetMap: OpenStreetMap is a free, editable map of the world, created and maintained by volunteers and available for use under an open license. It contains millions of data points, including roads, buildings, and points of interest. Source: https://www.openstreetmap.org/
2. Google Books Ngrams: Google Books Ngrams is a dataset of over 5 million books from Google Books, spanning from 1500 to 2008. It contains word counts for each year, allowing researchers to track the usage of words over time. Source: https://aws.amazon.com/datasets/google-books-ngrams/
3. Wikipedia: Wikipedia is a free, open-source encyclopedia with millions of articles in hundreds of languages. It is available for use under the Creative Commons Attribution-ShareAlike license. Source: https://www.wikipedia.org/
4. Common Crawl: Common Crawl is a large-scale web crawl that collects data from over 5 billion webpages. It is available for use under the Apache 2.0 license. Source: https://commoncrawl.org/
5. Open Images Dataset: The Open Images Dataset is a collection of 9 million images annotated with labels spanning over 6000 categories. It is available for use under the Apache 2.0 license. Source: https://storage.googleapis.com/openimages/web/index.html

completion = openai.Completion.create(engine="text-davinci-003", prompt="What are some emerging machine learning use-cases in social media?", max_tokens=1000, temperature=0)
print(completion.choices[0]['text'])
# output
1. Automated Content Curation: Automatically curating content from social media platforms to create personalized content feeds for users.
2. Sentiment Analysis: Analyzing user sentiment from social media posts to gain insights into customer opinions and preferences.
3. Social Media Monitoring: Using machine learning algorithms to monitor social media conversations and detect potential issues or trends.
4. Social Media Advertising: Leveraging machine learning to optimize social media advertising campaigns and target the right audience.
5. Social Media Recommendations: Using machine learning to recommend content to users based on their interests and preferences.

completion = openai.Completion.create(engine="text-davinci-003", prompt="What are some emerging machine learning use-cases in healthcare?", max_tokens=1000, temperature=0)
print(completion.choices[0]['text'])
# output
1. Automated Diagnosis: Machine learning algorithms can be used to analyze patient data and medical images to detect and diagnose diseases.
2. Personalized Medicine: Machine learning algorithms can be used to analyze patient data and medical images to create personalized treatment plans for each patient.
3. Drug Discovery: Machine learning algorithms can be used to analyze large datasets of chemical compounds to identify potential new drugs.
4. Clinical Decision Support: Machine learning algorithms can be used to analyze patient data and medical images to provide clinicians with real-time decision support.
5. Predictive Analytics: Machine learning algorithms can be used to analyze patient data and medical images to predict future health outcomes.

completion = openai.Completion.create(engine="text-davinci-003", prompt="What are some good research questions on using deep learning for image detection?", max_tokens=1000, temperature=0)
print(completion.choices[0]['text'])
# output
1. What are the most effective deep learning architectures for image detection?
2. How can deep learning be used to improve the accuracy of image detection?
3. What are the most effective methods for training deep learning models for image detection?
4. How can deep learning be used to detect objects in images with varying levels of complexity?
5. How can deep learning be used to detect objects in images with varying levels of illumination?
6. How can deep learning be used to detect objects in images with varying levels of noise?
7. How can deep learning be used to detect objects in images with varying levels of resolution?
8. How can deep learning be used to detect objects in images with varying levels of occlusion?
9. How can deep learning be used to detect objects in images with varying levels of background clutter?
10. How can deep learning be used to detect objects in images with varying levels of rotation?

completion = openai.Completion.create(engine="text-davinci-003", prompt="What are some good research questions related to NLP transformer models?", max_tokens=1000, temperature=0)
print(completion.choices[0]['text'])
# output
1. How can transformer models be used to improve the accuracy of natural language processing tasks?
2. What are the most effective methods for training transformer models for natural language processing tasks?
3. How can transformer models be used to improve the efficiency of natural language processing tasks?
4. What are the most effective methods for optimizing transformer models for natural language processing tasks?
5. How can transformer models be used to improve the interpretability of natural language processing tasks?
6. What are the most effective methods for deploying transformer models for natural language processing tasks?
7. How can transformer models be used to improve the scalability of natural language processing tasks?
8. What are the most effective methods for combining transformer models with other natural language processing techniques?
9. How can transformer models be used to improve the robustness of natural language processing tasks?
10. What are the most effective methods for evaluating transformer models for natural language processing tasks?