Sanoj111/mobile_prices

all_mobile_data5

Short description

all_mobile_data5 is a small CSV dataset of mobile phone models and their published prices in Nepal. It contains raw scraped price strings (with commas, ranges, and variants), a brand column, and a pre-parsed numeric "Price_clean" column that is partially populated. Use this dataset for experimentation with price-parsing, feature extraction, and simple price-prediction models.

Key facts

• Filename: all_mobile_data5.csv
• Columns: Model, Price, Brand, Price_clean
• Approximate rows: 127 (raw, includes header and informational rows)
• License: CC-BY-4.0 (recommended) — see LICENSE file

Column descriptions

• Model (string) — mobile model or descriptive text scraped from web pages (e.g., "Galaxy Z Fold 7", "12/256GB", or "Samsung Mobiles List").
• Price (string) — raw price field as scraped; may contain currency symbols, commas, multiple values, ranges, or storage/RAM annotations (e.g., Rs. 244,999 (12+256GB), NPR 239,999 (256GB), Price in Nepal).
• Brand (string) — scraped brand name or page context (e.g., "Samsung", "Apple", "Xiaomi", or page slugs).
• Price_clean (float or string) — a pre-parsed numeric price in some rows; many entries remain messy or inconsistent and should be validated/cleaned before use.

Usage examples

Loading with pandas:

import pandas as pd

df = pd.read_csv("all_mobile_data5.csv")
print(df.head())

from datasets import load_dataset

# If you uploaded a dataset repository that contains only this CSV:
ds = load_dataset("sanojDD/all_mobile_data5", data_files="all_mobile_data5.csv", split="train")
df = ds.to_pandas()

import re
def parse_price(price_str):
    s = str(price_str).replace(",", "")
    nums = re.findall(r"\d{4,7}", s)
    nums = [int(n) for n in nums if 5000 <= int(n) <= 500000]
    return nums[0] if nums else None



```text name=MIT
Creative Commons Attribution 4.0 International (CC BY 4.0)

You are free to:
- Share — copy and redistribute the material in any medium or format
- Adapt — remix, transform, and build upon the material for any purpose, even commercially.

Under the following terms:
- Attribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made.

Full license text: https://creativecommons.org/licenses/by/4.0/