├── .gitignore ├── Android Apps Google Play ├── Dataset │ ├── apps.csv │ └── user_reviews.csv └── Google_Play.ipynb ├── Nyc Airbnb ├── Exploring Airbnb Market Trends.ipynb └── datasets │ ├── airbnb_last_review.tsv │ ├── airbnb_price.csv │ └── airbnb_room_type.xlsx ├── README.md ├── Word Frequency in Classic Novels └── Word Frequency in Classic Novels.ipynb └── codewello-banner.png /.gitignore: -------------------------------------------------------------------------------- 1 | text-readme.txt -------------------------------------------------------------------------------- /Nyc Airbnb/Exploring Airbnb Market Trends.ipynb: -------------------------------------------------------------------------------- 1 | {"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"provenance":[],"authorship_tag":"ABX9TyO2OMYcvsNrWwnc8swuO0de"},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"}},"cells":[{"cell_type":"markdown","source":["#NYC Airbnb\n","\n","Welcome to NYC, a top tourist spot. Many Airbnb options cater to short or long stays. This notebook explores NYC's Airbnb scene using data from .csv, .tsv, and .xlsx files.\n","\n","\n","**the goal of this project**\n","\n","* What is the average price, per night, of an Airbnb listing in NYC?\n","* How does the average price of an Airbnb listing, per month, compare to the private rental market?\n","* How many adverts are for private rooms?\n","* How do Airbnb listing prices compare across the five NYC boroughs?\n","\n","\n","**We will be working with three datasets:**\n","\n","\"datasets/airbnb_price.csv\"\n","\n","\"datasets/airbnb_room_type.xlsx\"\n","\n","\"datasets/airbnb_last_review.tsv\"\n","\n"],"metadata":{"id":"o2plAFSi0QJU"}},{"cell_type":"markdown","source":["#1. Importing the data\n","\n","* Load \"data/airbnb_price.csv\" as a DataFrame called prices.\n","* Load \"data/airbnb_room_type.xlsx\" as a DataFrame called xls, and the first sheet from xls as a DataFrame called room_types.\n","* Load \"data/airbnb_last_review.tsv\" as a DataFrame called reviews."],"metadata":{"id":"_w4R7srY3_dN"}},{"cell_type":"code","execution_count":1,"metadata":{"id":"rP3jSI7h0DE2","executionInfo":{"status":"ok","timestamp":1700486267783,"user_tz":-120,"elapsed":650,"user":{"displayName":"Sam Eldin","userId":"17811646166394375086"}}},"outputs":[],"source":["import numpy as np\n","import pandas as pd\n","import datetime as dt"]},{"cell_type":"code","source":["#prices df\n","prices = pd.read_csv(\"/content/airbnb_price.csv\")\n","\n","#xls room types df\n","xls=pd.ExcelFile(\"/content/airbnb_room_type.xlsx\")\n","room_types = xls.parse(0)\n","\n","#rviews df\n","reviews= pd.read_csv(\"/content/airbnb_last_review.tsv\",sep='\\t')\n","\n","print(\n"," f\"prices: {prices.head()}\",\n"," \"\\n\",\n"," f\"room_types: {room_types.head()}\",\n"," \"\\n\",\n"," f\"reviews: {reviews.head()}\"\n",")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"OCX7pAvD4R37","executionInfo":{"status":"ok","timestamp":1700487667541,"user_tz":-120,"elapsed":2038,"user":{"displayName":"Sam Eldin","userId":"17811646166394375086"}},"outputId":"4b854687-802f-46d2-cbc9-0f09dc4003c3"},"execution_count":9,"outputs":[{"output_type":"stream","name":"stdout","text":["prices: listing_id price nbhood_full\n","0 2595 225 dollars Manhattan, Midtown\n","1 3831 89 dollars Brooklyn, Clinton Hill\n","2 5099 200 dollars Manhattan, Murray Hill\n","3 5178 79 dollars Manhattan, Hell's Kitchen\n","4 5238 150 dollars Manhattan, Chinatown \n"," room_types: Unnamed: 0 listing_id room_type number_of_reviews\n","0 0 2595 Entire home/apt 48\n","1 1 3831 Entire home/apt 295\n","2 2 5099 Entire home/apt 78\n","3 3 5121 Private room 49\n","4 4 5178 Private room 454 \n"," reviews: listing_id host_name last_review\n","0 2595 Jennifer May 21 2019\n","1 3831 LisaRoxanne July 05 2019\n","2 5099 Chris June 22 2019\n","3 5178 Shunichi June 24 2019\n","4 5238 Ben June 09 2019\n"]}]},{"cell_type":"markdown","source":["#Cleaning the price column\n","\n","Now the DataFrames have been loaded, the first step is to calculate the average price per listing by room_type.\n","\n","You may have noticed that the price column in the prices DataFrame currently states each value as a string with the currency (dollars) following, i.e.,\n","\n","price\n","225 dollars\n","89 dollars\n","200 dollars\n","We will need to clean the column in order to calculate the average price."],"metadata":{"id":"MhaA-LvK5pah"}},{"cell_type":"code","source":["# Remove whitespace and string characters from prices column\n","#prices[\"price\"] = prices[\"price\"].str.replace(\"dollars\",\"\")\n","\n","# Convert prices column to numeric datatype\n","#prices[\"price\"] = pd.to_numeric(prices[\"price\"])\n","\n","# Print head\n","print(prices[\"price\"].head())\n","\n","# Print descriptive statistics for the price column\n","print(prices[\"price\"].describe())"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"PGPxXlgE5o42","executionInfo":{"status":"ok","timestamp":1700487934695,"user_tz":-120,"elapsed":481,"user":{"displayName":"Sam Eldin","userId":"17811646166394375086"}},"outputId":"7a2de068-9970-46cf-ae4c-91755512738c"},"execution_count":13,"outputs":[{"output_type":"stream","name":"stdout","text":["0 225\n","1 89\n","2 200\n","3 79\n","4 150\n","Name: price, dtype: int64\n","count 25209.000000\n","mean 141.777936\n","std 147.349137\n","min 0.000000\n","25% 69.000000\n","50% 105.000000\n","75% 175.000000\n","max 7500.000000\n","Name: price, dtype: float64\n"]}]},{"cell_type":"markdown","source":["# 3. Calculating average price\n","\n","We can see three quarters of listings cost $175 per night or less.\n","\n","However, there are some outliers including a maximum price of $7,500 per night!\n","\n","Some of listings are actually showing as free. Let's remove these from the DataFrame, and calculate the average price."],"metadata":{"id":"IbhiH52L-uxz"}},{"cell_type":"code","source":["prices.info()\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"kUFGp-WD-1qZ","executionInfo":{"status":"ok","timestamp":1700487983616,"user_tz":-120,"elapsed":303,"user":{"displayName":"Sam Eldin","userId":"17811646166394375086"}},"outputId":"cccd3ed1-778d-411a-ce28-a9eb2e7904e4"},"execution_count":14,"outputs":[{"output_type":"stream","name":"stdout","text":["\n","RangeIndex: 25209 entries, 0 to 25208\n","Data columns (total 3 columns):\n"," # Column Non-Null Count Dtype \n","--- ------ -------------- ----- \n"," 0 listing_id 25209 non-null int64 \n"," 1 price 25209 non-null int64 \n"," 2 nbhood_full 25209 non-null object\n","dtypes: int64(2), object(1)\n","memory usage: 591.0+ KB\n"]}]},{"cell_type":"code","source":["# Subset prices for listings costing $0 named \"free_listings\"\n","free_listings = prices[\"price\"] == 0\n","print(type(free_listings))\n","print(free_listings.shape)\n","\n","# Update prices by removing all free listings from prices\n","# Similar to SQL's concept of \"NOT IN\"\n","prices = prices.loc[~free_listings]\n","\n","# Calculate the average price and round to nearest 2 decimal places, avg_price\n","avg_price = round(prices[\"price\"].mean(),2)\n","\n","# Print the average price\n","print(\"The average price per night for an Airbnb listing in NYC is ${}.\".format(avg_price))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"cB3pHCIr_CVa","executionInfo":{"status":"ok","timestamp":1700488032347,"user_tz":-120,"elapsed":3,"user":{"displayName":"Sam Eldin","userId":"17811646166394375086"}},"outputId":"673283b6-f452-440f-86d1-7c269cb1fd24"},"execution_count":15,"outputs":[{"output_type":"stream","name":"stdout","text":["\n","(25209,)\n","The average price per night for an Airbnb listing in NYC is $141.82.\n"]}]},{"cell_type":"markdown","source":["#4. Comparing costs to the private rental market\n","\n","Now we know how much a listing costs, on average, per night, but it would be useful to have a benchmark for comparison. According to Zumper, a 1 bedroom apartment in New York City costs, on average, $3,100 per month. Let's convert the per night prices of our listings into monthly costs, so we can compare to the private market.\n","\n"],"metadata":{"id":"n5dOjJB1_KHM"}},{"cell_type":"code","source":[],"metadata":{"id":"NA2gfKbQ_3OO"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["prices.head()\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":206},"id":"P394VJ4E_NQG","executionInfo":{"status":"ok","timestamp":1700488111462,"user_tz":-120,"elapsed":283,"user":{"displayName":"Sam Eldin","userId":"17811646166394375086"}},"outputId":"256180d1-9c3d-4805-bc11-f951d8383027"},"execution_count":17,"outputs":[{"output_type":"execute_result","data":{"text/plain":[" listing_id price nbhood_full\n","0 2595 225 Manhattan, Midtown\n","1 3831 89 Brooklyn, Clinton Hill\n","2 5099 200 Manhattan, Murray Hill\n","3 5178 79 Manhattan, Hell's Kitchen\n","4 5238 150 Manhattan, Chinatown"],"text/html":["\n","
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listing_idpricenbhood_full
02595225Manhattan, Midtown
1383189Brooklyn, Clinton Hill
25099200Manhattan, Murray Hill
3517879Manhattan, Hell's Kitchen
45238150Manhattan, Chinatown
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Unnamed: 0listing_idroom_typenumber_of_reviews
002595Entire home/apt48
113831Entire home/apt295
225099Entire home/apt78
335121Private room49
445178Private room454
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listing_idhost_namelast_review
02595JenniferMay 21 2019
13831LisaRoxanneJuly 05 2019
25099ChrisJune 22 2019
35178ShunichiJune 24 2019
45238BenJune 09 2019
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Joining the DataFrames."],"metadata":{"id":"C1FTilFEBOkQ"}},{"cell_type":"code","source":["# Merge prices and room_types to create rooms_and_prices\n","# https://pandas.pydata.org/docs/user_guide/merging.html\n","rooms_and_prices = pd.merge(prices, room_types,\n"," how=\"outer\",\n"," on=\"listing_id\")\n","\n","# Merge rooms_and_prices with the reviews DataFrame to create airbnb_merged\n","airbnb_merged = pd.merge(rooms_and_prices, reviews,\n"," how=\"outer\",\n"," on=\"listing_id\")\n","\n","# Drop missing values from airbnb_merged\n","airbnb_merged.dropna(inplace=True)\n","\n","# Check if there are any duplicate values\n","print(\"There are {} duplicates in the DataFrame.\".format(airbnb_merged.duplicated().sum()))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"AiGOY5suBSel","executionInfo":{"status":"ok","timestamp":1700488638839,"user_tz":-120,"elapsed":287,"user":{"displayName":"Sam Eldin","userId":"17811646166394375086"}},"outputId":"35dfd87e-b34a-4b0a-b940-c684ae0da7cb"},"execution_count":33,"outputs":[{"output_type":"stream","name":"stdout","text":["There are 0 duplicates in the DataFrame.\n"]}]},{"cell_type":"code","source":["print(airbnb_merged.info())\n","airbnb_merged.head()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":621},"id":"DHdAXXDlBZjh","executionInfo":{"status":"ok","timestamp":1700488651599,"user_tz":-120,"elapsed":15,"user":{"displayName":"Sam Eldin","userId":"17811646166394375086"}},"outputId":"c6c7f5b6-ecd8-486e-d793-5baf35ad5534"},"execution_count":34,"outputs":[{"output_type":"stream","name":"stdout","text":["\n","Int64Index: 13663 entries, 0 to 19192\n","Data columns (total 9 columns):\n"," # Column Non-Null Count Dtype \n","--- ------ -------------- ----- \n"," 0 listing_id 13663 non-null int64 \n"," 1 price 13663 non-null float64 \n"," 2 nbhood_full 13663 non-null object \n"," 3 price_per_month 13663 non-null float64 \n"," 4 Unnamed: 0 13663 non-null float64 \n"," 5 room_type 13663 non-null category \n"," 6 number_of_reviews 13663 non-null float64 \n"," 7 host_name 13663 non-null object \n"," 8 last_review 13663 non-null datetime64[ns]\n","dtypes: category(1), datetime64[ns](1), float64(4), int64(1), object(2)\n","memory usage: 974.2+ KB\n","None\n"]},{"output_type":"execute_result","data":{"text/plain":[" listing_id price nbhood_full price_per_month Unnamed: 0 \\\n","0 2595 225.0 Manhattan, Midtown 6843.750000 0.0 \n","1 3831 89.0 Brooklyn, Clinton Hill 2707.083333 1.0 \n","2 5099 200.0 Manhattan, Murray Hill 6083.333333 2.0 \n","3 5178 79.0 Manhattan, Hell's Kitchen 2402.916667 4.0 \n","4 5238 150.0 Manhattan, Chinatown 4562.500000 6.0 \n","\n"," room_type number_of_reviews host_name last_review \n","0 entire home/apt 48.0 Jennifer 2019-05-21 \n","1 entire home/apt 295.0 LisaRoxanne 2019-07-05 \n","2 entire home/apt 78.0 Chris 2019-06-22 \n","3 private room 454.0 Shunichi 2019-06-24 \n","4 entire home/apt 161.0 Ben 2019-06-09 "],"text/html":["\n","
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listing_idpricenbhood_fullprice_per_monthUnnamed: 0room_typenumber_of_reviewshost_namelast_review
02595225.0Manhattan, Midtown6843.7500000.0entire home/apt48.0Jennifer2019-05-21
1383189.0Brooklyn, Clinton Hill2707.0833331.0entire home/apt295.0LisaRoxanne2019-07-05
25099200.0Manhattan, Murray Hill6083.3333332.0entire home/apt78.0Chris2019-06-22
3517879.0Manhattan, Hell's Kitchen2402.9166674.0private room454.0Shunichi2019-06-24
45238150.0Manhattan, Chinatown4562.5000006.0entire home/apt161.0Ben2019-06-09
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\n"]},"metadata":{},"execution_count":34}]},{"cell_type":"markdown","source":["# 8. Analyzing listing prices by NYC borough"],"metadata":{"id":"VXk7iYeIBjSz"}},{"cell_type":"code","source":["# Extract information from the nbhood_full column and store as a new column, borough\n","# Either use `.str.partition()` or `.str.split()`\n","airbnb_merged[\"borough\"] = airbnb_merged[\"nbhood_full\"].str.partition(\",\", expand=True)[0]\n","\n","# Group by borough and calculate summary statistics\n","boroughs = airbnb_merged.groupby(\"borough\")[\"price\"].agg([\"sum\", \"mean\", \"median\", \"count\"])\n","\n","# Round boroughs to 2 decimal places, and sort by mean in descending order\n","boroughs = boroughs.round(2).sort_values(\"mean\", ascending=False)\n","\n","# Print boroughs\n","print(boroughs)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"oSVIOcycBoCi","executionInfo":{"status":"ok","timestamp":1700488734894,"user_tz":-120,"elapsed":273,"user":{"displayName":"Sam Eldin","userId":"17811646166394375086"}},"outputId":"932fe0fb-6a7b-4c2a-ea36-e3a26aeee394"},"execution_count":35,"outputs":[{"output_type":"stream","name":"stdout","text":[" sum mean median count\n","borough \n","Manhattan 893869.0 171.70 139.0 5206\n","Brooklyn 742816.0 123.02 100.0 6038\n","Queens 174429.0 92.73 73.0 1881\n","Staten Island 13439.0 86.15 70.5 156\n","Bronx 30954.0 81.03 63.5 382\n"]}]},{"cell_type":"markdown","source":["# 9. Price range by borough\n","\n","The above output gives us a summary of prices for listings across the 5 boroughs. In this final task we would like to categorize listings based on whether they fall into specific price ranges, and view this by borough.\n","\n","We can do this using percentiles and labels to create a new column, price_range, in the DataFrame. Once we have created the labels, we can then group the data and count frequencies for listings in each price range by borough.\n","\n","We will assign the following categories and price ranges:"],"metadata":{"id":"rh5Ne8ypB4Cz"}},{"cell_type":"code","source":["# Create labels for the price range, label_names\n","label_names = [\"Budget\", \"Average\", \"Expensive\", \"Extravagant\"]\n","\n","# Create the label ranges, ranges\n","ranges = [0, 69, 175, 350, np.inf]\n","\n","# Insert new column, price_range, into DataFrame\n","# Use `pd.cut` to segment and sort data values into bins\n","# Useful for going from a continuous variable to a categorical variable\n","airbnb_merged[\"price_range\"] = pd.cut(airbnb_merged[\"price\"], bins=ranges, labels=label_names)\n","\n","# Calculate borough and price_range frequencies, prices_by_borough\n","prices_by_borough = airbnb_merged.groupby([\"borough\", \"price_range\"])[\"price_range\"].agg(\"count\")\n","print(prices_by_borough)"],"metadata":{"id":"iqBzIdSLB9jo","executionInfo":{"status":"ok","timestamp":1700488799700,"user_tz":-120,"elapsed":9,"user":{"displayName":"Sam Eldin","userId":"17811646166394375086"}},"outputId":"9eac1d87-edd7-4707-9fed-226228d7bd8a","colab":{"base_uri":"https://localhost:8080/"}},"execution_count":36,"outputs":[{"output_type":"stream","name":"stdout","text":["borough price_range\n","Bronx Budget 209\n"," Average 155\n"," Expensive 14\n"," Extravagant 4\n","Brooklyn Budget 1697\n"," Average 3324\n"," Expensive 888\n"," Extravagant 129\n","Manhattan Budget 590\n"," Average 2868\n"," Expensive 1456\n"," Extravagant 292\n","Queens Budget 870\n"," Average 847\n"," Expensive 145\n"," Extravagant 19\n","Staten Island Budget 71\n"," Average 73\n"," Expensive 12\n"," Extravagant 0\n","Name: price_range, dtype: int64\n"]}]}]} -------------------------------------------------------------------------------- /Nyc Airbnb/datasets/airbnb_room_type.xlsx: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/HossamEldinx/NLP-Projects/fd79a6fdb9ee0b74be5b7dd64cd65708f471510d/Nyc Airbnb/datasets/airbnb_room_type.xlsx -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | ## NLP Projects with Data Analysis 2 | 3 |
4 |

5 | 6 | 10 | 11 |

12 |
13 | 14 | [Linkedin](https://www.linkedin.com/in/hossam-eldin/) | [Youtube](https://www.youtube.com/@codewello) | 15 | 16 | 17 |
18 |
19 | 20 | 21 | ## 👋 hello my name is hossam eldin 22 | 23 | 24 | I've put together this awesome list to help you and me create cool projects in NLP and data analysis! It's a step-by-step guide that's perfect for beginners to intermediate levels, using all sorts of libraries. Whether you're a newbie or a bit more experienced, you'll find this super useful. 25 | 26 | 27 | | **Num** | **Project** | **repository** | 28 | |:----:|:------------:|:-------------------------------------------------:| 29 | | 1 | [Ecommerce-review-sentiments](https://github.com/ninda-code/ecommerce-review-sentiments/tree/main) | [![GitHub](https://badges.aleen42.com/src/github.svg)](https://github.com/ninda-code/ecommerce-review-sentiments/tree/main) | 30 | | 2 | [NLP-Chatbot](https://github.com/ankitamasand/nlp-chatbot) | [![GitHub](https://badges.aleen42.com/src/github.svg)](https://github.com/ankitamasand/nlp-chatbot) | 31 | | 3 | [Topic-Modeling](https://github.com/yanhan-si/NLP-and-Topic-Modeling-on-User-Review-Dataset) | [![GitHub](https://badges.aleen42.com/src/github.svg)](https://github.com/yanhan-si/NLP-and-Topic-Modeling-on-User-Review-Dataset) | 32 | | 4 | [Text Summarization Using Spacy](https://github.com/sainivarsha97/spacy-Tutorial/tree/master) | [![GitHub](https://badges.aleen42.com/src/github.svg)](https://github.com/sainivarsha97/spacy-Tutorial/tree/master) | 33 | | 5 | [Spam Classification](https://github.com/manulthanura/Spam-Classification-using-NLP) | [![GitHub](https://badges.aleen42.com/src/github.svg)](https://github.com/manulthanura/Spam-Classification-using-NLP) | 34 | | 6 | [Text Classification](https://github.com/Idilismiguzel/NLP-with-Python/blob/master/Text-Classification.ipynb) | [![GitHub](https://badges.aleen42.com/src/github.svg)](https://github.com/Idilismiguzel/NLP-with-Python/blob/master/Text-Classification.ipynb) | 35 | | 7| [Basket Market Analysis ](https://github.com/limchiahooi/market-basket-analysis) | [![GitHub](https://badges.aleen42.com/src/github.svg)](https://github.com/limchiahooi/market-basket-analysis) | 36 | | 8 | [Question Tagging System](https://github.com/kushagra2103/Auto-Tagging-System) | [![GitHub](https://badges.aleen42.com/src/github.svg)](https://github.com/kushagra2103/Auto-Tagging-System) | 37 | | 9 | [Resume Parsing](https://github.com/anshulmahajan01/NLP/blob/master/Resume%20Parsing%20.ipynb) | [![GitHub](https://badges.aleen42.com/src/github.svg)](https://github.com/anshulmahajan01/NLP/blob/master/Resume%20Parsing%20.ipynb) | 38 | | 10 | [Disease Prediction](https://github.com/anujdutt9/Disease-Prediction-from-Symptoms) | [![GitHub](https://badges.aleen42.com/src/github.svg)](https://github.com/anujdutt9/Disease-Prediction-from-Symptoms) | 39 | | 11 | [Image-Caption-Generator](https://github.com/MiteshPuthran/Image-Caption-Generator/tree/master) | [![GitHub](https://badges.aleen42.com/src/github.svg)](https://github.com/MiteshPuthran/Image-Caption-Generator/tree/master) | 40 | | 12 | [Speech-Emotion-Analyzer](https://github.com/MiteshPuthran/Speech-Emotion-Analyzer) | [![GitHub](https://badges.aleen42.com/src/github.svg)](https://github.com/MiteshPuthran/Speech-Emotion-Analyzer) | 41 | | 13 | [Detecting Paraphrases](https://github.com/wasiahmad/paraphrase_identification) | [![GitHub](https://badges.aleen42.com/src/github.svg)](https://github.com/wasiahmad/paraphrase_identification) | 42 | | 14 | [Hate Speech Detection](https://github.com/NakulLakhotia/Hate-Speech-Detection-in-Social-Media-using-Python/blob/master/final_customization.ipynb) | [![GitHub](https://badges.aleen42.com/src/github.svg)](https://github.com/NakulLakhotia/Hate-Speech-Detection-in-Social-Media-using-Python/blob/master/final_customization.ipynb) | 43 | | 15 | [ The Android App Market on Google Play ](https://github.com/HossamEldinx/NLP-Projects/tree/main/Android%20Apps%20Google%20Play) | [![GitHub](https://badges.aleen42.com/src/github.svg)](https://github.com/HossamEldinx/NLP-Projects/tree/main/Android%20Apps%20Google%20Play) | 44 | | 16 | [Exploring the NYC Airbnb Market](https://github.com/HossamEldinx/NLP-Projects/tree/main/Nyc%20Airbnb) | [![GitHub](https://badges.aleen42.com/src/github.svg)](https://github.com/HossamEldinx/NLP-Projects/tree/main/Nyc%20Airbnb) | 45 | | 17 | [Word Frequency in Classic Novels](https://github.com/HossamEldinx/NLP-Projects/tree/main/Word%20Frequency%20in%20Classic%20Novels) | [![GitHub](https://badges.aleen42.com/src/github.svg)](https://github.com/HossamEldinx/NLP-Projects/tree/main/Word%20Frequency%20in%20Classic%20Novels) | 46 | | 18 | [Find Movie Similarity from Plot Summaries](https://github.com/mrbarkis/DataCamp_projects/tree/master/Find%20Movie%20Similarity%20from%20Plot%20Summaries) | [![GitHub](https://badges.aleen42.com/src/github.svg)](https://github.com/mrbarkis/DataCamp_projects/tree/master/Find%20Movie%20Similarity%20from%20Plot%20Summaries) | 47 | | 19 | [Real-time Insights from Social Media Data](https://github.com/mrbarkis/DataCamp_projects/tree/master/Real-time%20Insights%20from%20Social%20Media%20Data) | [![GitHub](https://badges.aleen42.com/src/github.svg)](https://github.com/mrbarkis/DataCamp_projects/tree/master/Real-time%20Insights%20from%20Social%20Media%20Data) | 48 | | 20 | [Classify Song Genres from Audio Data](https://github.com/kayveen/Classify-Song-Genres-from-Audio-Data) | [![GitHub](https://badges.aleen42.com/src/github.svg)](https://github.com/kayveen/Classify-Song-Genres-from-Audio-Data) | 49 | 50 | 51 | 52 | 53 | -------------------------------------------------------------------------------- /Word Frequency in Classic Novels/Word Frequency in Classic Novels.ipynb: -------------------------------------------------------------------------------- 1 | {"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"provenance":[],"authorship_tag":"ABX9TyNgaKv0fE/b2k8/tzFZTzek"},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"}},"cells":[{"cell_type":"markdown","source":["# 1. Tools for text processing\n","\n","We'll check which words are used the most in Herman Melville's Moby Dick and how often.\n","\n","We'll get the novel from Project Gutenberg using Python's requests.\n","with the Natural Language Toolkit (nltk)"],"metadata":{"id":"nbg_SKPWyVft"}},{"cell_type":"code","execution_count":1,"metadata":{"id":"OtLI6lsbyQyP","executionInfo":{"status":"ok","timestamp":1700551975655,"user_tz":-120,"elapsed":1732,"user":{"displayName":"Sam Eldin","userId":"17811646166394375086"}}},"outputs":[],"source":["# Importing requests, BeautifulSoup and nltk\n","import requests\n","from bs4 import BeautifulSoup\n","import nltk"]},{"cell_type":"markdown","source":["#2. Request Moby Dick\n","\n","HTML file: https://www.gutenberg.org/files/2701/2701-h/2701-h.htm ."],"metadata":{"id":"CZ5MOWWby9A_"}},{"cell_type":"code","source":["r= requests.get(\"https://s3.amazonaws.com/assets.datacamp.com/production/project_147/datasets/2701-h.htm\")\n","\n","r.encoding = 'utf-8'\n","\n","html = r.text\n","\n","html[:2000]"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":163},"id":"eYKCe_zUzARN","executionInfo":{"status":"ok","timestamp":1700552046352,"user_tz":-120,"elapsed":1097,"user":{"displayName":"Sam Eldin","userId":"17811646166394375086"}},"outputId":"9e36544b-7ba1-4448-fede-e4fb0efc3ba7"},"execution_count":2,"outputs":[{"output_type":"execute_result","data":{"text/plain":["'\\r\\n\\r\\n\\r\\n\\r\\n\\r\\n \\r\\n \\r\\n Moby Dick; Or the Whale, by Herman Melville\\r\\n \\r\\n \\r\\n \\r\\n \\r\\n
\\r\\n\\r\\nThe Project Gutenberg EBook of Moby Dick; or The Whale, by Herman Melville\\r\\n\\r\\nThis eBook is for the use of anyone anywh'"],"application/vnd.google.colaboratory.intrinsic+json":{"type":"string"}},"metadata":{},"execution_count":2}]},{"cell_type":"markdown","source":["# 3. Get the text from the HTML\n","\n"," For this we'll use the package BeautifulSoup\n",""],"metadata":{"id":"UFYL30OpzRhj"}},{"cell_type":"code","source":["soup = BeautifulSoup(html,\"lxml\")\n","\n","text = soup.get_text()\n","\n","text[32000:34000]\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":220},"id":"-8Bor8f4zbaV","executionInfo":{"status":"ok","timestamp":1700552109434,"user_tz":-120,"elapsed":461,"user":{"displayName":"Sam Eldin","userId":"17811646166394375086"}},"outputId":"95cd6ac7-10b5-47f4-ef00-8c7654f85f74"},"execution_count":3,"outputs":[{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/bs4/builder/__init__.py:545: XMLParsedAsHTMLWarning: It looks like you're parsing an XML document using an HTML parser. If this really is an HTML document (maybe it's XHTML?), you can ignore or filter this warning. If it's XML, you should know that using an XML parser will be more reliable. To parse this document as XML, make sure you have the lxml package installed, and pass the keyword argument `features=\"xml\"` into the BeautifulSoup constructor.\n","  warnings.warn(\n"]},{"output_type":"execute_result","data":{"text/plain":["'t me\\r\\n      from deliberately stepping into the street, and methodically knocking\\r\\n      people’s hats off—then, I account it high time to get to sea as soon\\r\\n      as I can. This is my substitute for pistol and ball. With a philosophical\\r\\n      flourish Cato throws himself upon his sword; I quietly take to the ship.\\r\\n      There is nothing surprising in this. If they but knew it, almost all men\\r\\n      in their degree, some time or other, cherish very nearly the same feelings\\r\\n      towards the ocean with me.\\r\\n    \\n\\r\\n      There now is your insular city of the Manhattoes, belted round by wharves\\r\\n      as Indian isles by coral reefs—commerce surrounds it with her surf.\\r\\n      Right and left, the streets take you waterward. Its extreme downtown is\\r\\n      the battery, where that noble mole is washed by waves, and cooled by\\r\\n      breezes, which a few hours previous were out of sight of land. Look at the\\r\\n      crowds of water-gazers there.\\r\\n    \\n\\r\\n      Circumambulate the city of a dreamy Sabbath afternoon. Go from Corlears\\r\\n      Hook to Coenties Slip, and from thence, by Whitehall, northward. What do\\r\\n      you see?—Posted like silent sentinels all around the town, stand\\r\\n      thousands upon thousands of mortal men fixed in ocean reveries. Some\\r\\n      leaning against the spiles; some seated upon the pier-heads; some looking\\r\\n      over the bulwarks of ships from China; some high aloft in the rigging, as\\r\\n      if striving to get a still better seaward peep. But these are all\\r\\n      landsmen; of week days pent up in lath and plaster—tied to counters,\\r\\n      nailed to benches, clinched to desks. How then is this? Are the green\\r\\n      fields gone? What do they here?\\r\\n    \\n\\r\\n      But look! here come more crowds, pacing straight for the water, and\\r\\n      seemingly bound for a dive. Strange! Nothing will content them but the\\r\\n      extremest limit of the land; loitering under the shady lee of yonder\\r\\n      warehouses will not suffice. No. They must get just as nigh the '"],"application/vnd.google.colaboratory.intrinsic+json":{"type":"string"}},"metadata":{},"execution_count":3}]},{"cell_type":"markdown","source":["# 4. Extract the words\n","\n","For how to use the nltk.tokenize.RegexpTokenizer function, please see the example in [the nltk documentation.](https://www.nltk.org/api/nltk.tokenize.html?highlight=regexp#module-nltk.tokenize.regexp)\n","\n"],"metadata":{"id":"vVxenmwUzl5F"}},{"cell_type":"code","source":["tokenizer = nltk.tokenize.RegexpTokenizer(pattern='\\w+')\n","\n","tokens = tokenizer.tokenize(text=text)\n","\n","tokens[:10]"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"MBg5xCzCz9jv","executionInfo":{"status":"ok","timestamp":1700552257582,"user_tz":-120,"elapsed":5,"user":{"displayName":"Sam Eldin","userId":"17811646166394375086"}},"outputId":"852f330e-9a55-44fe-c50b-5052bc3ec4ce"},"execution_count":4,"outputs":[{"output_type":"execute_result","data":{"text/plain":["['Moby',\n"," 'Dick',\n"," 'Or',\n"," 'the',\n"," 'Whale',\n"," 'by',\n"," 'Herman',\n"," 'Melville',\n"," 'The',\n"," 'Project']"]},"metadata":{},"execution_count":4}]},{"cell_type":"markdown","source":["#5. Make the words lowercase"],"metadata":{"id":"3-y5BGiI0HV5"}},{"cell_type":"code","source":["words = [token.lower() for token in tokens]\n","\n","# Printing out the first 8 words / tokens\n","words[:8]"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"yHHdKFVy0Mxj","executionInfo":{"status":"ok","timestamp":1700552301088,"user_tz":-120,"elapsed":297,"user":{"displayName":"Sam Eldin","userId":"17811646166394375086"}},"outputId":"d3b7d20f-5a6c-47da-b70b-0a6a8f01ae01"},"execution_count":5,"outputs":[{"output_type":"execute_result","data":{"text/plain":["['moby', 'dick', 'or', 'the', 'whale', 'by', 'herman', 'melville']"]},"metadata":{},"execution_count":5}]},{"cell_type":"markdown","source":["#6. Load in stop words\n","\n","People often take out common English words like 'the,' 'of,' and 'a' because they're not very interesting. These words are called stop words. The nltk package has a helpful list of stop words in English that we can use.\n","\n","\n","\n","\n","\n"],"metadata":{"id":"ODi-qvfL0SFj"}},{"cell_type":"code","source":["#get stopwords\n","nltk.download('stopwords')\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"FslNuOos0fnT","executionInfo":{"status":"ok","timestamp":1700552414919,"user_tz":-120,"elapsed":282,"user":{"displayName":"Sam Eldin","userId":"17811646166394375086"}},"outputId":"2daebed5-9b36-4d7a-99c1-3604021e3629"},"execution_count":6,"outputs":[{"output_type":"stream","name":"stderr","text":["[nltk_data] Downloading package stopwords to /root/nltk_data...\n","[nltk_data]   Unzipping corpora/stopwords.zip.\n"]},{"output_type":"execute_result","data":{"text/plain":["True"]},"metadata":{},"execution_count":6}]},{"cell_type":"code","source":["sw = nltk.corpus.stopwords.words('english')\n"],"metadata":{"id":"w5tfvN9g0qTF","executionInfo":{"status":"ok","timestamp":1700552438435,"user_tz":-120,"elapsed":397,"user":{"displayName":"Sam Eldin","userId":"17811646166394375086"}}},"execution_count":8,"outputs":[]},{"cell_type":"code","source":["sw[:15]"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"gfhDX9Qh0tbK","executionInfo":{"status":"ok","timestamp":1700552440002,"user_tz":-120,"elapsed":4,"user":{"displayName":"Sam Eldin","userId":"17811646166394375086"}},"outputId":"d052e096-cad1-43d9-ef88-ff5ea7bc4b81"},"execution_count":9,"outputs":[{"output_type":"execute_result","data":{"text/plain":["['i',\n"," 'me',\n"," 'my',\n"," 'myself',\n"," 'we',\n"," 'our',\n"," 'ours',\n"," 'ourselves',\n"," 'you',\n"," \"you're\",\n"," \"you've\",\n"," \"you'll\",\n"," \"you'd\",\n"," 'your',\n"," 'yours']"]},"metadata":{},"execution_count":9}]},{"cell_type":"markdown","source":["# 7. Remove stop words in Moby Dick\n","\n","We want to make a list of words from Moby Dick, but without using certain common words (stop words). To do this, we'll go through each word in the original list and add it to a new list only if it's not a stop word.\n","\n","\n","\n","\n","\n"],"metadata":{"id":"3wF50USg0xbC"}},{"cell_type":"code","source":["words_ns = [word for word in words if word not in sw]\n","\n","words_ns[:5]\n","\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"m2Y3nkuG00Yw","executionInfo":{"status":"ok","timestamp":1700552474918,"user_tz":-120,"elapsed":462,"user":{"displayName":"Sam Eldin","userId":"17811646166394375086"}},"outputId":"3b76c2ff-3bae-42cf-e45d-64426b2fc7d5"},"execution_count":10,"outputs":[{"output_type":"execute_result","data":{"text/plain":["['moby', 'dick', 'whale', 'herman', 'melville']"]},"metadata":{},"execution_count":10}]},{"cell_type":"markdown","source":["#8. We have the answer\n","\n","Our original question was:\n","\n","What are the most frequent words in Herman Melville's novel Moby Dick and how often do they occur?\n","\n","We are now ready to answer that! Let's create a word frequency distribution plot using nltk.\n","\n","See the nltk documentation for how to use nltk.FreqDist()\n"],"metadata":{"id":"DujdVu2B1EmH"}},{"cell_type":"code","source":["%matplotlib inline"],"metadata":{"id":"6hJTtkx11y7K","executionInfo":{"status":"ok","timestamp":1700552724427,"user_tz":-120,"elapsed":2,"user":{"displayName":"Sam Eldin","userId":"17811646166394375086"}}},"execution_count":11,"outputs":[]},{"cell_type":"code","source":["freqdist = nltk.FreqDist(words_ns)\n","\n","freqdist.plot(25)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":508},"id":"prJqvsq010qa","executionInfo":{"status":"ok","timestamp":1700552734225,"user_tz":-120,"elapsed":1209,"user":{"displayName":"Sam Eldin","userId":"17811646166394375086"}},"outputId":"c6f086dd-8e12-487b-a76c-4540c048a94c"},"execution_count":12,"outputs":[{"output_type":"display_data","data":{"text/plain":["
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\n"},"metadata":{}},{"output_type":"execute_result","data":{"text/plain":[""]},"metadata":{},"execution_count":12}]},{"cell_type":"code","source":["import matplotlib.pyplot as plt\n","\n","freqdist = nltk.FreqDist(words_ns)\n","\n","top_words = freqdist.most_common(25)\n","\n","words, frequencies = zip(*top_words)\n","\n","most_common_word, most_common_frequency = freqdist.most_common(1)[0]\n","\n","# Create a bar chart\n","plt.figure(figsize=(10, 6))\n","plt.bar(range(len(words)), frequencies, tick_label=words)\n","plt.xlabel('Words')\n","plt.ylabel('Frequency')\n","plt.title('Top 25 Words Frequency Distribution')\n","plt.xticks(rotation=45, ha='right')  # Rotate x-axis labels for better visibility\n","plt.tight_layout()\n","plt.show()\n","\n","print(f\"The most common word is '{most_common_word}' with a frequency of {most_common_frequency}.\")\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":625},"id":"7r5MpQHl3JUu","executionInfo":{"status":"ok","timestamp":1700553632017,"user_tz":-120,"elapsed":874,"user":{"displayName":"Sam Eldin","userId":"17811646166394375086"}},"outputId":"82163bdc-ee5a-4145-88d6-69b64b523697"},"execution_count":15,"outputs":[{"output_type":"display_data","data":{"text/plain":["
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\n"},"metadata":{}},{"output_type":"stream","name":"stdout","text":["The most common word is 'whale' with a frequency of 1246.\n"]}]}]}


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