creat a program in python using bigml api. The program has to load a dataset named diabetes.csv and, using this dataset, has to create a logistic regression model to predict if a patient has diabetes
 Sure, here's an example Python program that uses the BigML API to load the "diabetes.csv" dataset and create a logistic regression model to predict if a patient has diabetes:
```python
import bigml.api
import bigml.config

# Set up BigML API configuration
config = bigml.config.load_config()
api = bigml.api.BigML()

# Create a new source (dataset) from the local file "diabetes.csv"
source = api.create_source(config, "diabetes.csv")
print("Created source (dataset) with ID:", source["object"]["resource"])

# Create a new dataset from the source
dataset = api.create_dataset(config, source["object"]["resource"])
print("Created dataset with ID:", dataset["object"]["resource"])

# Create a new model using logistic regression algorithm
model = api.create_model(config, dataset["object"]["resource"], {"algorithm": "logisticregression"})
print("Created model with ID:", model["object"]["resource"])

# Create a new prediction querying the model
prediction = api.create_prediction(config, model["object"]["resource"], {"input": {"pregnancies": 5, "glucose": 148, "blood_pressure":
  Sure! Here is an example program in Python that uses the BigML API to load a dataset named "diabetes.csv" and create a logistic regression model to predict if a patient has diabetes:
```
import pandas as pd
import bigml

# Load the diabetes.csv dataset
df = pd.read_csv("diabetes.csv")

# Create a BigML account and load the dataset
bigml = bigml.BigML(username="your_bigml_username", password="your_bigml_password")
dataset = bigml.load_dataset("diabetes.csv")

# Split the dataset into training and testing sets
train_size = int(len(df) * 0.8)
train_df = df.iloc[0:train_size]
test_df = df.iloc[train_size:]

# Create a logistic regression model
model = bigml.LogisticRegression()

# Train the model on the training data
model.train(train_df, train_size)

# Evaluate the model on the testing data
predictions = model.predict(test_df)

# Print the accuracy of the model
accuracy = model.accuracy(test_df)
print("Accuracy:", accuracy)

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