Bennykillua

{
    "type": "AdaptiveCard",
    "$schema": "http://adaptivecards.io/schemas/adaptive-card.json",
    "version": "1.2",
    "body": [
        {
            "type": "TextBlock",
            "text": "Task Management System",
            "wrap": true,
            "id": "title",

Bennykillua

Bennykillua

Bennykillua

Bennykillua

pip install scikit-llm

Bennykillua

# Install and import the Comet Library:
import comet_ml
from comet_ml import Artifact, Experiment

# Initialize comet instance for API Key. You can access your API Key here: https://www.comet.com/account-settings/apiKeys
comet_ml.init()

# Create an Experiment object by giving it a name and the workspace it should belong to. 
# You can get all your available workspaces here: https://www.comet.com/account-settings/workspaces

Bennykillua

from pandasai.helpers.openai_info import get_openai_callback
with get_openai_callback() as cb:
response = sdf.chat("Calculate the average cost of rent in Delhi")
print(response)
print(cb)

Bennykillua

from pandasai import SmartDataframe
sdf = SmartDataframe(df, config={"llm": llm})

Bennykillua

# This command will impute missing values in your data frame.
sdf.impute_missing_values()

# To ask your data questions using PandaAI, you use sdf.chat

sdf.chat("who is the ideal tenant for a 3 bedroom in kolkata")

sdf.chat("Return the top 5 expensive city by rent")

sdf.chat("In a table show me the average rent in the various cities each month and group this data by the BHK") #BHK here represent number of bedrooms, hall, and kitchen

Bennykillua

import pandas as pd
# House rent dataset link: https://www.kaggle.com/datasets/iamsouravbanerjee/house-rent-prediction-dataset
df = pd. read_csv('/content/House_Rent_Dataset.csv')
df.head(5)