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Table of Contents
Introduction to the Machine Learning Project Exploring Indian Cuisine Best Analysis
The dataset called Indian Food Recipes is a comprehensive collection of culinary information gathered mainly from Archana’s Kitchen website.
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With a total of 6871 entries, this dataset provides a diverse range of recipes that cover different cuisines, courses, and dietary preferences.
Each entry in the dataset contains detailed information such as the recipe name, ingredients, preparation and cooking times, servings, cuisine type, course category, dietary specifications, and cooking instructions.
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Additionally, the dataset includes both original content and translated versions using the Google Translate API, ensuring accessibility and consistency.
This dataset is a valuable resource for the analytics community interested in exploring various aspects of Indian cuisine.
Data Overview
The dataset is made up of 15 columns, with a serial number (Srno) for indexing. Here is the link: https://www.kaggle.com/datasets/sukhmandeepsinghbrar/indian-food-dataset/data
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Important columns like RecipeName, Ingredients, PrepTimeInMins, CookTimeInMins, TotalTimeInMins, Servings, Cuisine, Course, Diet, Instructions, and their translations offer crucial details for analysis and visualization.
It’s worth mentioning that the dataset encompasses a wide array of cuisines, ranging from North Indian, South Indian, Mughlai, to Continental, catering to diverse culinary tastes.
Exploratory Data Analysis (EDA)
When analyzing the data, we can uncover various findings from the dataset. Visual representations show us how cuisines, courses, and dietary preferences are distributed, giving us a clearer picture of what the dataset consists of.
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Moreover, histograms show us the breakdown of preparation, cooking, and total times, providing valuable information on the time needed for various recipes.
Box plots also help us understand the differences in serving sizes among cuisines and how the type of course affects the total cooking time.
Insights and Trends
Analysis of the dataset highlights several interesting trends and patterns within Indian cuisine. For instance, North Indian cuisine appears to be dominant among the recorded recipes, reflecting its popularity across the country and beyond. Furthermore, vegetarian recipes outnumber non-vegetarian ones, indicating a preference for plant-based diets among consumers. The dataset also sheds light on the diversity of courses, ranging from appetizers and main courses to desserts, catering to various dining occasions and preferences.
import pandas as pd import matplotlib.pyplot as plt import seaborn as sns import warnings warnings.filterwarnings('ignore')
df = pd.read_csv("/kaggle/input/indian-food-dataset/IndianFoodDatasetCSV.csv")
df.head()
Srno | RecipeName | TranslatedRecipeName | Ingredients | TranslatedIngredients | PrepTimeInMins | CookTimeInMins | TotalTimeInMins | Servings | Cuisine | Course | Diet | Instructions | TranslatedInstructions | URL | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0 | 1 | Masala Karela Recipe | Masala Karela Recipe | 6 Karela (Bitter Gourd/ Pavakkai) – deseeded,S… | 6 Karela (Bitter Gourd/ Pavakkai) – deseeded,S… | 15 | 30 | 45 | 6 | Indian | Side Dish | Diabetic Friendly | To begin making the Masala Karela Recipe,de-se… | To begin making the Masala Karela Recipe,de-se… | https://www.archanaskitchen.com/masala-karela-… |
1 | 2 | टमाटर पुलियोगरे रेसिपी – Spicy Tomato Rice (Re… | Spicy Tomato Rice (Recipe) | 2-1/2 कप चावल – पका ले,3 टमाटर,3 छोटा चमच्च बी… | 2-1 / 2 cups rice – cooked, 3 tomatoes, 3 teas… | 5 | 10 | 15 | 3 | South Indian Recipes | Main Course | Vegetarian | टमाटर पुलियोगरे बनाने के लिए सबसे पहले टमाटर क… | To make tomato puliogere, first cut the tomato… | http://www.archanaskitchen.com/spicy-tomato-ri… |
2 | 3 | Ragi Semiya Upma Recipe – Ragi Millet Vermicel… | Ragi Semiya Upma Recipe – Ragi Millet Vermicel… | 1-1/2 cups Rice Vermicelli Noodles (Thin),1 On… | 1-1/2 cups Rice Vermicelli Noodles (Thin),1 On… | 20 | 30 | 50 | 4 | South Indian Recipes | South Indian Breakfast | High Protein Vegetarian | To begin making the Ragi Vermicelli Recipe, fi… | To begin making the Ragi Vermicelli Recipe, fi… | http://www.archanaskitchen.com/ragi-vermicelli… |
3 | 4 | Gongura Chicken Curry Recipe – Andhra Style Go… | Gongura Chicken Curry Recipe – Andhra Style Go… | 500 grams Chicken,2 Onion – chopped,1 Tomato -… | 500 grams Chicken,2 Onion – chopped,1 Tomato -… | 15 | 30 | 45 | 4 | Andhra | Lunch | Non Vegeterian | To begin making Gongura Chicken Curry Recipe f… | To begin making Gongura Chicken Curry Recipe f… | http://www.archanaskitchen.com/gongura-chicken… |
4 | 5 | आंध्रा स्टाइल आलम पचड़ी रेसिपी – Adrak Chutney … | Andhra Style Alam Pachadi Recipe – Adrak Chutn… | 1 बड़ा चमच्च चना दाल,1 बड़ा चमच्च सफ़ेद उरद दाल,2… | 1 tablespoon chana dal, 1 tablespoon white ura… | 10 | 20 | 30 | 4 | Andhra | South Indian Breakfast | Vegetarian | आंध्रा स्टाइल आलम पचड़ी बनाने के लिए सबसे पहले … | To make Andhra Style Alam Pachadi, first heat … | https://www.archanaskitchen.com/andhra-style-a… |
df.describe()
Srno | PrepTimeInMins | CookTimeInMins | TotalTimeInMins | Servings | |
---|---|---|---|---|---|
count | 6871.000000 | 6871.000000 | 6871.000000 | 6871.000000 | 6871.000000 |
mean | 4902.979042 | 28.585213 | 30.832339 | 59.417552 | 5.611410 |
std | 3479.531022 | 81.042007 | 34.019694 | 88.699940 | 26.221807 |
min | 1.000000 | 0.000000 | 0.000000 | 0.000000 | 1.000000 |
25% | 1955.500000 | 10.000000 | 20.000000 | 30.000000 | 4.000000 |
50% | 4304.000000 | 15.000000 | 30.000000 | 40.000000 | 4.000000 |
75% | 7363.000000 | 20.000000 | 35.000000 | 55.000000 | 4.000000 |
max | 14211.000000 | 2880.000000 | 900.000000 | 2925.000000 | 1000.000000 |
plt.figure(figsize=(15, 6)) df['Cuisine'].value_counts().plot(kind='bar') plt.title('Distribution of Cuisine') plt.xlabel('Cuisine') plt.ylabel('Count') plt.xticks(rotation=90) plt.show()
![](https://www.kaggleusercontent.com/kf/175729325/eyJhbGciOiJkaXIiLCJlbmMiOiJBMTI4Q0JDLUhTMjU2In0..ooP-g4Hi0oVlMgTUGSWwpA.THTzRfhsASyB0w9hR2U4gTEzMLh4LSlM_wTYz4wRxJW4hO0m7alp6c9swUrHhMXJ36X_NDcud6zeLAE7F5P6w2OdeagacZTKSL_rxB7i-TL508Yuce6yyuAW1WznhD5IiY-bpDsHugUlHyydw3cefTlDTDkCPmcULyA3dLQDuhJ342pK_-bIburpwfMaS_yjCTCkcorjuuHu_5QP3TNMa11KIA3PlAGnQuvA8j3g7bIQXmFR9Wek1KecURfWMGsi2uydPrv7_YMLhMAwvtt2Ti0xHT8n1u8QeqpkSVEn9WGP20lKJY3Qr1_oaWNgLa0TJhjdT7Tm8ASd72LQ1SCI1ewxuj65lTpNQ7n24myz8nMAys3Mhah17cZyoCnY27M1hhqIWaA2IEpbqvhmJLB9OwmCkxJ25bpeBk4xNKvh0c5L9S00VsxcQb2kqDR4aqUSseMF5ULzHTvw2K5Hha9BHQoY6zSHAl1bTlfnADyKQ7dDhXkL1xb1634pyYOG-XvRtxQVPQe-UOJoCrsWviVQsw5ZZWJ73MSI_MVpkQQ67roMSYHNtHxa1yjscUsla-McuMNAIjarrP5Nn7Y-7XRSr1KyxH7ojZhYMWxyMWb_ckfHkrukoydAQyTWpr1W8SX7jkYvu7kx8JGe5hnk9FQWB9JgMUr_xmboiJvyBZkQcht8JMeaDLNstfZYI4VU96If.7jBIBq3E4EnCgUch1aPclw/__results___files/__results___9_0.png)
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plt.figure(figsize=(10, 6)) sns.histplot(df['PrepTimeInMins'], bins=20, kde=True) plt.title('Preparation Time Distribution') plt.xlabel('Time (minutes)') plt.ylabel('Frequency') plt.show()
![](https://www.kaggleusercontent.com/kf/175729325/eyJhbGciOiJkaXIiLCJlbmMiOiJBMTI4Q0JDLUhTMjU2In0..ooP-g4Hi0oVlMgTUGSWwpA.THTzRfhsASyB0w9hR2U4gTEzMLh4LSlM_wTYz4wRxJW4hO0m7alp6c9swUrHhMXJ36X_NDcud6zeLAE7F5P6w2OdeagacZTKSL_rxB7i-TL508Yuce6yyuAW1WznhD5IiY-bpDsHugUlHyydw3cefTlDTDkCPmcULyA3dLQDuhJ342pK_-bIburpwfMaS_yjCTCkcorjuuHu_5QP3TNMa11KIA3PlAGnQuvA8j3g7bIQXmFR9Wek1KecURfWMGsi2uydPrv7_YMLhMAwvtt2Ti0xHT8n1u8QeqpkSVEn9WGP20lKJY3Qr1_oaWNgLa0TJhjdT7Tm8ASd72LQ1SCI1ewxuj65lTpNQ7n24myz8nMAys3Mhah17cZyoCnY27M1hhqIWaA2IEpbqvhmJLB9OwmCkxJ25bpeBk4xNKvh0c5L9S00VsxcQb2kqDR4aqUSseMF5ULzHTvw2K5Hha9BHQoY6zSHAl1bTlfnADyKQ7dDhXkL1xb1634pyYOG-XvRtxQVPQe-UOJoCrsWviVQsw5ZZWJ73MSI_MVpkQQ67roMSYHNtHxa1yjscUsla-McuMNAIjarrP5Nn7Y-7XRSr1KyxH7ojZhYMWxyMWb_ckfHkrukoydAQyTWpr1W8SX7jkYvu7kx8JGe5hnk9FQWB9JgMUr_xmboiJvyBZkQcht8JMeaDLNstfZYI4VU96If.7jBIBq3E4EnCgUch1aPclw/__results___files/__results___10_0.png)
plt.figure(figsize=(10, 6)) sns.histplot(df['CookTimeInMins'], bins=20, kde=True) plt.title('Cooking Time Distribution') plt.xlabel('Time (minutes)') plt.ylabel('Frequency') plt.show()
![](https://www.kaggleusercontent.com/kf/175729325/eyJhbGciOiJkaXIiLCJlbmMiOiJBMTI4Q0JDLUhTMjU2In0..ooP-g4Hi0oVlMgTUGSWwpA.THTzRfhsASyB0w9hR2U4gTEzMLh4LSlM_wTYz4wRxJW4hO0m7alp6c9swUrHhMXJ36X_NDcud6zeLAE7F5P6w2OdeagacZTKSL_rxB7i-TL508Yuce6yyuAW1WznhD5IiY-bpDsHugUlHyydw3cefTlDTDkCPmcULyA3dLQDuhJ342pK_-bIburpwfMaS_yjCTCkcorjuuHu_5QP3TNMa11KIA3PlAGnQuvA8j3g7bIQXmFR9Wek1KecURfWMGsi2uydPrv7_YMLhMAwvtt2Ti0xHT8n1u8QeqpkSVEn9WGP20lKJY3Qr1_oaWNgLa0TJhjdT7Tm8ASd72LQ1SCI1ewxuj65lTpNQ7n24myz8nMAys3Mhah17cZyoCnY27M1hhqIWaA2IEpbqvhmJLB9OwmCkxJ25bpeBk4xNKvh0c5L9S00VsxcQb2kqDR4aqUSseMF5ULzHTvw2K5Hha9BHQoY6zSHAl1bTlfnADyKQ7dDhXkL1xb1634pyYOG-XvRtxQVPQe-UOJoCrsWviVQsw5ZZWJ73MSI_MVpkQQ67roMSYHNtHxa1yjscUsla-McuMNAIjarrP5Nn7Y-7XRSr1KyxH7ojZhYMWxyMWb_ckfHkrukoydAQyTWpr1W8SX7jkYvu7kx8JGe5hnk9FQWB9JgMUr_xmboiJvyBZkQcht8JMeaDLNstfZYI4VU96If.7jBIBq3E4EnCgUch1aPclw/__results___files/__results___11_0.png)
plt.figure(figsize=(10, 6)) sns.histplot(df['TotalTimeInMins'], bins=20, kde=True) plt.title('Total Time Distribution') plt.xlabel('Time (minutes)') plt.ylabel('Frequency') plt.show()
![](https://www.kaggleusercontent.com/kf/175729325/eyJhbGciOiJkaXIiLCJlbmMiOiJBMTI4Q0JDLUhTMjU2In0..ooP-g4Hi0oVlMgTUGSWwpA.THTzRfhsASyB0w9hR2U4gTEzMLh4LSlM_wTYz4wRxJW4hO0m7alp6c9swUrHhMXJ36X_NDcud6zeLAE7F5P6w2OdeagacZTKSL_rxB7i-TL508Yuce6yyuAW1WznhD5IiY-bpDsHugUlHyydw3cefTlDTDkCPmcULyA3dLQDuhJ342pK_-bIburpwfMaS_yjCTCkcorjuuHu_5QP3TNMa11KIA3PlAGnQuvA8j3g7bIQXmFR9Wek1KecURfWMGsi2uydPrv7_YMLhMAwvtt2Ti0xHT8n1u8QeqpkSVEn9WGP20lKJY3Qr1_oaWNgLa0TJhjdT7Tm8ASd72LQ1SCI1ewxuj65lTpNQ7n24myz8nMAys3Mhah17cZyoCnY27M1hhqIWaA2IEpbqvhmJLB9OwmCkxJ25bpeBk4xNKvh0c5L9S00VsxcQb2kqDR4aqUSseMF5ULzHTvw2K5Hha9BHQoY6zSHAl1bTlfnADyKQ7dDhXkL1xb1634pyYOG-XvRtxQVPQe-UOJoCrsWviVQsw5ZZWJ73MSI_MVpkQQ67roMSYHNtHxa1yjscUsla-McuMNAIjarrP5Nn7Y-7XRSr1KyxH7ojZhYMWxyMWb_ckfHkrukoydAQyTWpr1W8SX7jkYvu7kx8JGe5hnk9FQWB9JgMUr_xmboiJvyBZkQcht8JMeaDLNstfZYI4VU96If.7jBIBq3E4EnCgUch1aPclw/__results___files/__results___12_0.png)
numeric_cols = ['PrepTimeInMins', 'CookTimeInMins', 'TotalTimeInMins', 'Servings'] plt.figure(figsize=(10, 8)) sns.heatmap(df[numeric_cols].corr(), annot=True, cmap='coolwarm') plt.title('Correlation Heatmap of Numeric Variables') plt.show()
![](https://www.kaggleusercontent.com/kf/175729325/eyJhbGciOiJkaXIiLCJlbmMiOiJBMTI4Q0JDLUhTMjU2In0..ooP-g4Hi0oVlMgTUGSWwpA.THTzRfhsASyB0w9hR2U4gTEzMLh4LSlM_wTYz4wRxJW4hO0m7alp6c9swUrHhMXJ36X_NDcud6zeLAE7F5P6w2OdeagacZTKSL_rxB7i-TL508Yuce6yyuAW1WznhD5IiY-bpDsHugUlHyydw3cefTlDTDkCPmcULyA3dLQDuhJ342pK_-bIburpwfMaS_yjCTCkcorjuuHu_5QP3TNMa11KIA3PlAGnQuvA8j3g7bIQXmFR9Wek1KecURfWMGsi2uydPrv7_YMLhMAwvtt2Ti0xHT8n1u8QeqpkSVEn9WGP20lKJY3Qr1_oaWNgLa0TJhjdT7Tm8ASd72LQ1SCI1ewxuj65lTpNQ7n24myz8nMAys3Mhah17cZyoCnY27M1hhqIWaA2IEpbqvhmJLB9OwmCkxJ25bpeBk4xNKvh0c5L9S00VsxcQb2kqDR4aqUSseMF5ULzHTvw2K5Hha9BHQoY6zSHAl1bTlfnADyKQ7dDhXkL1xb1634pyYOG-XvRtxQVPQe-UOJoCrsWviVQsw5ZZWJ73MSI_MVpkQQ67roMSYHNtHxa1yjscUsla-McuMNAIjarrP5Nn7Y-7XRSr1KyxH7ojZhYMWxyMWb_ckfHkrukoydAQyTWpr1W8SX7jkYvu7kx8JGe5hnk9FQWB9JgMUr_xmboiJvyBZkQcht8JMeaDLNstfZYI4VU96If.7jBIBq3E4EnCgUch1aPclw/__results___files/__results___13_0.png)
plt.figure(figsize=(15, 8)) sns.boxplot(x='Cuisine', y='Servings', data=df) plt.title('Boxplot of Servings by Cuisine') plt.xticks(rotation=90) plt.show()
![](https://www.kaggleusercontent.com/kf/175729325/eyJhbGciOiJkaXIiLCJlbmMiOiJBMTI4Q0JDLUhTMjU2In0..ooP-g4Hi0oVlMgTUGSWwpA.THTzRfhsASyB0w9hR2U4gTEzMLh4LSlM_wTYz4wRxJW4hO0m7alp6c9swUrHhMXJ36X_NDcud6zeLAE7F5P6w2OdeagacZTKSL_rxB7i-TL508Yuce6yyuAW1WznhD5IiY-bpDsHugUlHyydw3cefTlDTDkCPmcULyA3dLQDuhJ342pK_-bIburpwfMaS_yjCTCkcorjuuHu_5QP3TNMa11KIA3PlAGnQuvA8j3g7bIQXmFR9Wek1KecURfWMGsi2uydPrv7_YMLhMAwvtt2Ti0xHT8n1u8QeqpkSVEn9WGP20lKJY3Qr1_oaWNgLa0TJhjdT7Tm8ASd72LQ1SCI1ewxuj65lTpNQ7n24myz8nMAys3Mhah17cZyoCnY27M1hhqIWaA2IEpbqvhmJLB9OwmCkxJ25bpeBk4xNKvh0c5L9S00VsxcQb2kqDR4aqUSseMF5ULzHTvw2K5Hha9BHQoY6zSHAl1bTlfnADyKQ7dDhXkL1xb1634pyYOG-XvRtxQVPQe-UOJoCrsWviVQsw5ZZWJ73MSI_MVpkQQ67roMSYHNtHxa1yjscUsla-McuMNAIjarrP5Nn7Y-7XRSr1KyxH7ojZhYMWxyMWb_ckfHkrukoydAQyTWpr1W8SX7jkYvu7kx8JGe5hnk9FQWB9JgMUr_xmboiJvyBZkQcht8JMeaDLNstfZYI4VU96If.7jBIBq3E4EnCgUch1aPclw/__results___files/__results___14_0.png)
plt.figure(figsize=(10, 6)) sns.boxplot(x='Course', y='TotalTimeInMins', data=df) plt.title('Boxplot of Total Time by Course') plt.xticks(rotation=90) plt.show()
![](https://www.kaggleusercontent.com/kf/175729325/eyJhbGciOiJkaXIiLCJlbmMiOiJBMTI4Q0JDLUhTMjU2In0..ooP-g4Hi0oVlMgTUGSWwpA.THTzRfhsASyB0w9hR2U4gTEzMLh4LSlM_wTYz4wRxJW4hO0m7alp6c9swUrHhMXJ36X_NDcud6zeLAE7F5P6w2OdeagacZTKSL_rxB7i-TL508Yuce6yyuAW1WznhD5IiY-bpDsHugUlHyydw3cefTlDTDkCPmcULyA3dLQDuhJ342pK_-bIburpwfMaS_yjCTCkcorjuuHu_5QP3TNMa11KIA3PlAGnQuvA8j3g7bIQXmFR9Wek1KecURfWMGsi2uydPrv7_YMLhMAwvtt2Ti0xHT8n1u8QeqpkSVEn9WGP20lKJY3Qr1_oaWNgLa0TJhjdT7Tm8ASd72LQ1SCI1ewxuj65lTpNQ7n24myz8nMAys3Mhah17cZyoCnY27M1hhqIWaA2IEpbqvhmJLB9OwmCkxJ25bpeBk4xNKvh0c5L9S00VsxcQb2kqDR4aqUSseMF5ULzHTvw2K5Hha9BHQoY6zSHAl1bTlfnADyKQ7dDhXkL1xb1634pyYOG-XvRtxQVPQe-UOJoCrsWviVQsw5ZZWJ73MSI_MVpkQQ67roMSYHNtHxa1yjscUsla-McuMNAIjarrP5Nn7Y-7XRSr1KyxH7ojZhYMWxyMWb_ckfHkrukoydAQyTWpr1W8SX7jkYvu7kx8JGe5hnk9FQWB9JgMUr_xmboiJvyBZkQcht8JMeaDLNstfZYI4VU96If.7jBIBq3E4EnCgUch1aPclw/__results___files/__results___15_0.png)
sns.pairplot(df[numeric_cols]) plt.suptitle('Pairplot of Numeric Variables', y=1.02) plt.show()
![](https://www.kaggleusercontent.com/kf/175729325/eyJhbGciOiJkaXIiLCJlbmMiOiJBMTI4Q0JDLUhTMjU2In0..ooP-g4Hi0oVlMgTUGSWwpA.THTzRfhsASyB0w9hR2U4gTEzMLh4LSlM_wTYz4wRxJW4hO0m7alp6c9swUrHhMXJ36X_NDcud6zeLAE7F5P6w2OdeagacZTKSL_rxB7i-TL508Yuce6yyuAW1WznhD5IiY-bpDsHugUlHyydw3cefTlDTDkCPmcULyA3dLQDuhJ342pK_-bIburpwfMaS_yjCTCkcorjuuHu_5QP3TNMa11KIA3PlAGnQuvA8j3g7bIQXmFR9Wek1KecURfWMGsi2uydPrv7_YMLhMAwvtt2Ti0xHT8n1u8QeqpkSVEn9WGP20lKJY3Qr1_oaWNgLa0TJhjdT7Tm8ASd72LQ1SCI1ewxuj65lTpNQ7n24myz8nMAys3Mhah17cZyoCnY27M1hhqIWaA2IEpbqvhmJLB9OwmCkxJ25bpeBk4xNKvh0c5L9S00VsxcQb2kqDR4aqUSseMF5ULzHTvw2K5Hha9BHQoY6zSHAl1bTlfnADyKQ7dDhXkL1xb1634pyYOG-XvRtxQVPQe-UOJoCrsWviVQsw5ZZWJ73MSI_MVpkQQ67roMSYHNtHxa1yjscUsla-McuMNAIjarrP5Nn7Y-7XRSr1KyxH7ojZhYMWxyMWb_ckfHkrukoydAQyTWpr1W8SX7jkYvu7kx8JGe5hnk9FQWB9JgMUr_xmboiJvyBZkQcht8JMeaDLNstfZYI4VU96If.7jBIBq3E4EnCgUch1aPclw/__results___files/__results___16_0.png)
Recommendations and Future Directions
The Indian Food Recipes dataset is a valuable resource for culinary analysis, offering plenty of opportunities for enrichment and exploration.
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To make it even better, we can expand the dataset by adding more attributes like nutritional information, regional variations, and user ratings.
By collaborating with domain experts and food enthusiasts, we can ensure the dataset is authentic and comprehensive, allowing us to gain a deeper understanding of Indian culinary traditions and practices.
# Distribution of cuisines, focusing on the top 10 cuisines top_cuisines = df['Cuisine'].value_counts().nlargest(10).index plt.figure(figsize=(10, 6)) sns.countplot(y='Cuisine', data=df[df['Cuisine'].isin(top_cuisines)],order=top_cuisines) plt.title('Distribution of Top 10 Cuisines') plt.xlabel('Count') plt.ylabel('Cuisine') plt.grid(axis='x', linestyle='--', alpha=0.5,color='black') plt.show()
![](https://www.kaggleusercontent.com/kf/177075211/eyJhbGciOiJkaXIiLCJlbmMiOiJBMTI4Q0JDLUhTMjU2In0..KqN33ctJPL_nHoQtlLSfpQ.zSilruEKIi8GB_1Buc3jlH7FdOvY3i0rXkWh05B3kOJb9w-KjZCGkZ176Cc4I7iCExZJbgOt8VqRdPAIsL8eFkgxVrSXdC689QCknQL9DllEjP5UaOAAf_YC6-cisSitF72pBwMnKeHdvFWpbrhVG9cqpp_wlzoKpETZPaRhrKrO7sv4wTQMuSAHloU9Z3Yh_xirrrOo0ia0Ha6B9GiWlbCINtI0Bca0SPHtN3CVGgOXhVntx0tC99i1_Z8NEVGwf16Uroy0K4Yl7pCELJ-AhSjGOcTGBkHiMx8dbDnKtNruvcu1_MZM_mxgplNXoLxGuBw9lTm4q5M7mZrSLY3_rJML5Okea2JrPQbl-195kB5z9tiS0QR7ECCKO0rV_kAyHGkVyI7OjZunV_SEpa1V6t5e86Rn4spVhiVv-dcLTTaRzxlCk540XeUw1mG8oE4xGPLO0l10MsSZG5ZfNmZVJE44DCnIrx1CNCx5attWzdGCpIXZ7sGk-qlSQQLUyXuWP8UMINqzqZXXrtmlC7hZbKH56Q7rN-bqstlBZx290adzpeuhx2O-jFrHFQ9GyFLQCWBw656rIH-Yq06oyaii6CTNc8Bl-j5DL8Y-FGHhxl-M--SZIH0kU2tXNqN88lU3.FqmIaTeF9gq54H2mmw3IuA/__results___files/__results___9_0.png)
# Distribution of courses, focusing on the top 10 courses top_courses = df['Course'].value_counts().nlargest(10).index plt.figure(figsize=(8, 5)) sns.countplot(x='Course', data=df[df['Course'].isin(top_courses)],order=top_courses) plt.title('Distribution of Top 10 Courses') plt.xlabel('Course') plt.ylabel('Count') plt.xticks(rotation=45) plt.grid(axis='y', linestyle='--', alpha=0.5,color='black') plt.show()
![](https://www.kaggleusercontent.com/kf/177075211/eyJhbGciOiJkaXIiLCJlbmMiOiJBMTI4Q0JDLUhTMjU2In0..KqN33ctJPL_nHoQtlLSfpQ.zSilruEKIi8GB_1Buc3jlH7FdOvY3i0rXkWh05B3kOJb9w-KjZCGkZ176Cc4I7iCExZJbgOt8VqRdPAIsL8eFkgxVrSXdC689QCknQL9DllEjP5UaOAAf_YC6-cisSitF72pBwMnKeHdvFWpbrhVG9cqpp_wlzoKpETZPaRhrKrO7sv4wTQMuSAHloU9Z3Yh_xirrrOo0ia0Ha6B9GiWlbCINtI0Bca0SPHtN3CVGgOXhVntx0tC99i1_Z8NEVGwf16Uroy0K4Yl7pCELJ-AhSjGOcTGBkHiMx8dbDnKtNruvcu1_MZM_mxgplNXoLxGuBw9lTm4q5M7mZrSLY3_rJML5Okea2JrPQbl-195kB5z9tiS0QR7ECCKO0rV_kAyHGkVyI7OjZunV_SEpa1V6t5e86Rn4spVhiVv-dcLTTaRzxlCk540XeUw1mG8oE4xGPLO0l10MsSZG5ZfNmZVJE44DCnIrx1CNCx5attWzdGCpIXZ7sGk-qlSQQLUyXuWP8UMINqzqZXXrtmlC7hZbKH56Q7rN-bqstlBZx290adzpeuhx2O-jFrHFQ9GyFLQCWBw656rIH-Yq06oyaii6CTNc8Bl-j5DL8Y-FGHhxl-M--SZIH0kU2tXNqN88lU3.FqmIaTeF9gq54H2mmw3IuA/__results___files/__results___10_0.png)
# Distribution of diets, focusing on the top 10 diets top_diets = df['Diet'].value_counts().nlargest(10).index plt.figure(figsize=(8, 5)) sns.countplot(y='Diet', data=df[df['Diet'].isin(top_diets)],order=top_diets) plt.title('Distribution of Top 10 Diets') plt.xlabel('Diet') plt.ylabel('Count') plt.xticks(rotation=90) plt.show()
![](https://www.kaggleusercontent.com/kf/177075211/eyJhbGciOiJkaXIiLCJlbmMiOiJBMTI4Q0JDLUhTMjU2In0..KqN33ctJPL_nHoQtlLSfpQ.zSilruEKIi8GB_1Buc3jlH7FdOvY3i0rXkWh05B3kOJb9w-KjZCGkZ176Cc4I7iCExZJbgOt8VqRdPAIsL8eFkgxVrSXdC689QCknQL9DllEjP5UaOAAf_YC6-cisSitF72pBwMnKeHdvFWpbrhVG9cqpp_wlzoKpETZPaRhrKrO7sv4wTQMuSAHloU9Z3Yh_xirrrOo0ia0Ha6B9GiWlbCINtI0Bca0SPHtN3CVGgOXhVntx0tC99i1_Z8NEVGwf16Uroy0K4Yl7pCELJ-AhSjGOcTGBkHiMx8dbDnKtNruvcu1_MZM_mxgplNXoLxGuBw9lTm4q5M7mZrSLY3_rJML5Okea2JrPQbl-195kB5z9tiS0QR7ECCKO0rV_kAyHGkVyI7OjZunV_SEpa1V6t5e86Rn4spVhiVv-dcLTTaRzxlCk540XeUw1mG8oE4xGPLO0l10MsSZG5ZfNmZVJE44DCnIrx1CNCx5attWzdGCpIXZ7sGk-qlSQQLUyXuWP8UMINqzqZXXrtmlC7hZbKH56Q7rN-bqstlBZx290adzpeuhx2O-jFrHFQ9GyFLQCWBw656rIH-Yq06oyaii6CTNc8Bl-j5DL8Y-FGHhxl-M--SZIH0kU2tXNqN88lU3.FqmIaTeF9gq54H2mmw3IuA/__results___files/__results___11_0.png)
# Distribution of servings, focusing on the top 20 servings plt.figure(figsize=(8, 5)) top_servings = df['Servings'].value_counts().nlargest(20) sns.barplot(x=top_servings.index, y=top_servings.values,order=top_servings.index) plt.title('Distribution of Top 20 Servings') plt.xlabel('Servings') plt.ylabel('Count') plt.xticks(rotation=45) plt.show()
![](https://www.kaggleusercontent.com/kf/177075211/eyJhbGciOiJkaXIiLCJlbmMiOiJBMTI4Q0JDLUhTMjU2In0..KqN33ctJPL_nHoQtlLSfpQ.zSilruEKIi8GB_1Buc3jlH7FdOvY3i0rXkWh05B3kOJb9w-KjZCGkZ176Cc4I7iCExZJbgOt8VqRdPAIsL8eFkgxVrSXdC689QCknQL9DllEjP5UaOAAf_YC6-cisSitF72pBwMnKeHdvFWpbrhVG9cqpp_wlzoKpETZPaRhrKrO7sv4wTQMuSAHloU9Z3Yh_xirrrOo0ia0Ha6B9GiWlbCINtI0Bca0SPHtN3CVGgOXhVntx0tC99i1_Z8NEVGwf16Uroy0K4Yl7pCELJ-AhSjGOcTGBkHiMx8dbDnKtNruvcu1_MZM_mxgplNXoLxGuBw9lTm4q5M7mZrSLY3_rJML5Okea2JrPQbl-195kB5z9tiS0QR7ECCKO0rV_kAyHGkVyI7OjZunV_SEpa1V6t5e86Rn4spVhiVv-dcLTTaRzxlCk540XeUw1mG8oE4xGPLO0l10MsSZG5ZfNmZVJE44DCnIrx1CNCx5attWzdGCpIXZ7sGk-qlSQQLUyXuWP8UMINqzqZXXrtmlC7hZbKH56Q7rN-bqstlBZx290adzpeuhx2O-jFrHFQ9GyFLQCWBw656rIH-Yq06oyaii6CTNc8Bl-j5DL8Y-FGHhxl-M--SZIH0kU2tXNqN88lU3.FqmIaTeF9gq54H2mmw3IuA/__results___files/__results___12_0.png)
# Distribution of prep time, focusing on the top 20 prep times plt.figure(figsize=(10, 6)) top_prep_times = df['PrepTimeInMins'].value_counts().nlargest(20) sns.barplot(x=top_prep_times.index, y=top_prep_times.values,order=top_prep_times.index) plt.title('Distribution of Top 20 Prep Times (in minutes)') plt.xlabel('Prep Time (minutes)') plt.ylabel('Count') plt.xticks(rotation=45) plt.show()
![](https://www.kaggleusercontent.com/kf/177075211/eyJhbGciOiJkaXIiLCJlbmMiOiJBMTI4Q0JDLUhTMjU2In0..KqN33ctJPL_nHoQtlLSfpQ.zSilruEKIi8GB_1Buc3jlH7FdOvY3i0rXkWh05B3kOJb9w-KjZCGkZ176Cc4I7iCExZJbgOt8VqRdPAIsL8eFkgxVrSXdC689QCknQL9DllEjP5UaOAAf_YC6-cisSitF72pBwMnKeHdvFWpbrhVG9cqpp_wlzoKpETZPaRhrKrO7sv4wTQMuSAHloU9Z3Yh_xirrrOo0ia0Ha6B9GiWlbCINtI0Bca0SPHtN3CVGgOXhVntx0tC99i1_Z8NEVGwf16Uroy0K4Yl7pCELJ-AhSjGOcTGBkHiMx8dbDnKtNruvcu1_MZM_mxgplNXoLxGuBw9lTm4q5M7mZrSLY3_rJML5Okea2JrPQbl-195kB5z9tiS0QR7ECCKO0rV_kAyHGkVyI7OjZunV_SEpa1V6t5e86Rn4spVhiVv-dcLTTaRzxlCk540XeUw1mG8oE4xGPLO0l10MsSZG5ZfNmZVJE44DCnIrx1CNCx5attWzdGCpIXZ7sGk-qlSQQLUyXuWP8UMINqzqZXXrtmlC7hZbKH56Q7rN-bqstlBZx290adzpeuhx2O-jFrHFQ9GyFLQCWBw656rIH-Yq06oyaii6CTNc8Bl-j5DL8Y-FGHhxl-M--SZIH0kU2tXNqN88lU3.FqmIaTeF9gq54H2mmw3IuA/__results___files/__results___13_0.png)
# Similarly, for cook time, focusing on the top 20 cook times plt.figure(figsize=(10, 6)) top_cook_times = df['CookTimeInMins'].value_counts().nlargest(20) sns.barplot(x=top_cook_times.index, y=top_cook_times.values,order=top_cook_times.index) plt.title('Distribution of Top 20 Cook Times (in minutes)') plt.xlabel('Cook Time (minutes)') plt.ylabel('Count') plt.xticks(rotation=45) plt.show()
![](https://www.kaggleusercontent.com/kf/177075211/eyJhbGciOiJkaXIiLCJlbmMiOiJBMTI4Q0JDLUhTMjU2In0..KqN33ctJPL_nHoQtlLSfpQ.zSilruEKIi8GB_1Buc3jlH7FdOvY3i0rXkWh05B3kOJb9w-KjZCGkZ176Cc4I7iCExZJbgOt8VqRdPAIsL8eFkgxVrSXdC689QCknQL9DllEjP5UaOAAf_YC6-cisSitF72pBwMnKeHdvFWpbrhVG9cqpp_wlzoKpETZPaRhrKrO7sv4wTQMuSAHloU9Z3Yh_xirrrOo0ia0Ha6B9GiWlbCINtI0Bca0SPHtN3CVGgOXhVntx0tC99i1_Z8NEVGwf16Uroy0K4Yl7pCELJ-AhSjGOcTGBkHiMx8dbDnKtNruvcu1_MZM_mxgplNXoLxGuBw9lTm4q5M7mZrSLY3_rJML5Okea2JrPQbl-195kB5z9tiS0QR7ECCKO0rV_kAyHGkVyI7OjZunV_SEpa1V6t5e86Rn4spVhiVv-dcLTTaRzxlCk540XeUw1mG8oE4xGPLO0l10MsSZG5ZfNmZVJE44DCnIrx1CNCx5attWzdGCpIXZ7sGk-qlSQQLUyXuWP8UMINqzqZXXrtmlC7hZbKH56Q7rN-bqstlBZx290adzpeuhx2O-jFrHFQ9GyFLQCWBw656rIH-Yq06oyaii6CTNc8Bl-j5DL8Y-FGHhxl-M--SZIH0kU2tXNqN88lU3.FqmIaTeF9gq54H2mmw3IuA/__results___files/__results___14_0.png)
Indian Food ML Prediction
# This Python 3 environment comes with many helpful analytics libraries installed # It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python # For example, here's several helpful packages to load import numpy as np # linear algebra import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv) # Input data files are available in the read-only "../input/" directory # For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory import os for dirname, _, filenames in os.walk('/kaggle/input'): for filename in filenames: print(os.path.join(dirname, filename)) # You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using "Save & Run All" # You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session
import pandas as pd import numpy as np import matplotlib.pyplot as plt import seaborn as sns import warnings warnings.filterwarnings("ignore")
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Load Data
df align= pd.read_csv('/kaggle/input/indian-food-dataset/IndianFoodDatasetCSV.csv')
df.head()
Srno | RecipeName | TranslatedRecipeName | Ingredients | TranslatedIngredients | PrepTimeInMins | CookTimeInMins | TotalTimeInMins | Servings | Cuisine | Course | Diet | Instructions | TranslatedInstructions | URL | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0 | 1 | Masala Karela Recipe | Masala Karela Recipe | 6 Karela (Bitter Gourd/ Pavakkai) – deseeded,S… | 6 Karela (Bitter Gourd/ Pavakkai) – deseeded,S… | 15 | 30 | 45 | 6 | Indian | Side Dish | Diabetic Friendly | To begin making the Masala Karela Recipe,de-se… | To begin making the Masala Karela Recipe,de-se… | https://www.archanaskitchen.com/masala-karela-… |
1 | 2 | टमाटर पुलियोगरे रेसिपी – Spicy Tomato Rice (Re… | Spicy Tomato Rice (Recipe) | 2-1/2 कप चावल – पका ले,3 टमाटर,3 छोटा चमच्च बी… | 2-1 / 2 cups rice – cooked, 3 tomatoes, 3 teas… | 5 | 10 | 15 | 3 | South Indian Recipes | Main Course | Vegetarian | टमाटर पुलियोगरे बनाने के लिए सबसे पहले टमाटर क… | To make tomato puliogere, first cut the tomato… | http://www.archanaskitchen.com/spicy-tomato-ri… |
2 | 3 | Ragi Semiya Upma Recipe – Ragi Millet Vermicel… | Ragi Semiya Upma Recipe – Ragi Millet Vermicel… | 1-1/2 cups Rice Vermicelli Noodles (Thin),1 On… | 1-1/2 cups Rice Vermicelli Noodles (Thin),1 On… | 20 | 30 | 50 | 4 | South Indian Recipes | South Indian Breakfast | High Protein Vegetarian | To begin making the Ragi Vermicelli Recipe, fi… | To begin making the Ragi Vermicelli Recipe, fi… | http://www.archanaskitchen.com/ragi-vermicelli… |
3 | 4 | Gongura Chicken Curry Recipe – Andhra Style Go… | Gongura Chicken Curry Recipe – Andhra Style Go… | 500 grams Chicken,2 Onion – chopped,1 Tomato -… | 500 grams Chicken,2 Onion – chopped,1 Tomato -… | 15 | 30 | 45 | 4 | Andhra | Lunch | Non Vegeterian | To begin making Gongura Chicken Curry Recipe f… | To begin making Gongura Chicken Curry Recipe f… | http://www.archanaskitchen.com/gongura-chicken… |
4 | 5 | आंध्रा स्टाइल आलम पचड़ी रेसिपी – Adrak Chutney … | Andhra Style Alam Pachadi Recipe – Adrak Chutn… | 1 बड़ा चमच्च चना दाल,1 बड़ा चमच्च सफ़ेद उरद दाल,2… | 1 tablespoon chana dal, 1 tablespoon white ura… | 10 | 20 | 30 | 4 | Andhra | South Indian Breakfast | Vegetarian | आंध्रा स्टाइल आलम पचड़ी बनाने के लिए सबसे पहले … | To make Andhra Style Alam Pachadi, first heat … | https://www.archanaskitchen.com/andhra-style-a… |
df.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 6871 entries, 0 to 6870 Data columns (total 15 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 Srno 6871 non-null int64 1 RecipeName 6871 non-null object 2 TranslatedRecipeName 6871 non-null object 3 Ingredients 6865 non-null object 4 TranslatedIngredients 6865 non-null object 5 PrepTimeInMins 6871 non-null int64 6 CookTimeInMins 6871 non-null int64 7 TotalTimeInMins 6871 non-null int64 8 Servings 6871 non-null int64 9 Cuisine 6871 non-null object 10 Course 6871 non-null object 11 Diet 6871 non-null object 12 Instructions 6871 non-null object 13 TranslatedInstructions 6871 non-null object 14 URL 6871 non-null object dtypes: int64(5), object(10) memory usage: 805.3+ KB
df.isnull().sum()
Srno 0 RecipeName 0 TranslatedRecipeName 0 Ingredients 6 TranslatedIngredients 6 PrepTimeInMins 0 CookTimeInMins 0 TotalTimeInMins 0 Servings 0 Cuisine 0 Course 0 Diet 0 Instructions 0 TranslatedInstructions 0 URL 0 dtype: int64
Analysis
df['Cuisine'].unique()
array(['Indian', 'South Indian Recipes', 'Andhra', 'Udupi', 'Mexican', 'Fusion', 'Continental', 'Bengali Recipes', 'Punjabi', 'Chettinad', 'Tamil Nadu', 'Maharashtrian Recipes', 'North Indian Recipes', 'Italian Recipes', 'Sindhi', 'Thai', 'Chinese', 'Kerala Recipes', 'Gujarati Recipes\ufeff', 'Coorg', 'Rajasthani', 'Asian', 'Middle Eastern', 'Coastal Karnataka', 'European', 'Kashmiri', 'Karnataka', 'Lucknowi', 'Hyderabadi', 'Side Dish', 'Goan Recipes', 'Arab', 'Assamese', 'Bihari', 'Malabar', 'Himachal', 'Awadhi', 'Cantonese', 'North East India Recipes', 'Sichuan', 'Mughlai', 'Japanese', 'Mangalorean', 'Vietnamese', 'British', 'North Karnataka', 'Parsi Recipes', 'Greek', 'Nepalese', 'Oriya Recipes', 'French', 'Indo Chinese', 'Konkan', 'Mediterranean', 'Sri Lankan', 'Haryana', 'Uttar Pradesh', 'Malvani', 'Indonesian', 'African', 'Shandong', 'Korean', 'American', 'Kongunadu', 'Pakistani', 'Caribbean', 'South Karnataka', 'Appetizer', 'Uttarakhand-North Kumaon', 'World Breakfast', 'Malaysian', 'Dessert', 'Hunan', 'Dinner', 'Snack', 'Jewish', 'Burmese', 'Afghan', 'Brunch', 'Jharkhand', 'Nagaland', 'Lunch'], dtype=object)
df['Diet'].unique()
array(['Diabetic Friendly', 'Vegetarian', 'High Protein Vegetarian', 'Non Vegeterian', 'High Protein Non Vegetarian', 'Eggetarian', 'Vegan', 'No Onion No Garlic (Sattvic)', 'Gluten Free', 'Sugar Free Diet'], dtype=object)
df['Course'].unique()
array(['Side Dish', 'Main Course', 'South Indian Breakfast', 'Lunch', 'Snack', 'High Protein Vegetarian', 'Dinner', 'Appetizer', 'Indian Breakfast', 'Dessert', 'North Indian Breakfast', 'One Pot Dish', 'World Breakfast', 'Non Vegeterian', 'Vegetarian', 'Eggetarian', 'No Onion No Garlic (Sattvic)', 'Brunch', 'Vegan', 'Sugar Free Diet'], dtype=object)
Preprocessing
What will we do next after looking at the data ?
We will try to remove the rows which are not completely translated to english to make it ready for ML suggestion model
pd.set_option('display.max_colwidth', None) df['TranslatedIngredients']
0 6 Karela (Bitter Gourd/ Pavakkai) - deseeded,Salt - to taste,1 Onion - thinly sliced,3 tablespoon Gram flour (besan),2 teaspoons Turmeric powder (Haldi),1 tablespoon Red Chilli powder,2 teaspoons Cumin seeds (Jeera),1 tablespoon Coriander Powder (Dhania),1 tablespoon Amchur (Dry Mango Powder),Sunflower Oil - as required 1 2-1 / 2 cups rice - cooked, 3 tomatoes, 3 teaspoons BC Belle Bhat powder, salt - as per taste, 1 teaspoon chickpea lentils, 1/2 teaspoon cumin seeds, 1 teaspoon white urad dal, 1/2 Teaspoon mustard, 1 green chilli, 1 dry red chilli, 2 teaspoon cashew - or peanuts, 1-1 / 2 tablespoon oil - 1/2 teaspoon asafoetida 2 1-1/2 cups Rice Vermicelli Noodles (Thin),1 Onion - sliced,1/2 cup Carrots (Gajjar) - chopped,1/3 cup Green peas (Matar),2 Green Chillies,1/4 teaspoon Asafoetida (hing),1 teaspoon Mustard seeds,1 teaspoon White Urad Dal (Split),1 teaspoon Ghee,1 sprig Curry leaves,Salt - to taste,1 teaspoon Lemon juice 3 500 grams Chicken,2 Onion - chopped,1 Tomato - chopped,4 Green Chillies - slit,1 inch Ginger - finely chopped,6 cloves Garlic - finely chopped,1/2 teaspoon Turmeric powder (Haldi),1 teaspoon Garam masala powder,2 tablespoon Sesame (Gingelly) Oil,Salt - to taste,1/4 teaspoon Methi Seeds (Fenugreek Seeds),1 tablespoon Coriander (Dhania) Seeds,4 Dry Red Chillies,1 teaspoon Fennel seeds (Saunf),1 teaspoon Sesame (Gingelly) Oil,4 cloves Garlic,2 cups Sorrel Leaves (Gongura) - picked and chopped,Salt - to taste 4 1 tablespoon chana dal, 1 tablespoon white urad dal, 2 red chillies, 1 tablespoon coriander seeds, 3 inches ginger - chop, 1 onion - chop, 1 tomato - chop, salt - as per taste, 1 Teaspoon mustard, asafoetida - a pinch, oil - as per use, 1 sprig curry ... 6866 20 बटन मशरुम,2 प्याज - काट ले,1 टमाटर - बारीक काट ले,3 टहनी हरा धनिया - बारीक काट ले,1/2 कप नारियल - कस ले,4 सुखी लाल मिर्च - क्रश कर ले,1 छोटा चम्मच धनिये के बीज,5 पूरी काली मिर्च,2 लॉन्ग,1 छोटा चम्मच हल्दी पाउडर,1 छोटा चम्मच गरम मसाला पाउडर,1/2 इमली - छोटा सा,नमक - स्वाद अनुसार,3 बड़े चम्मच तेल 6867 1 बड़ा चम्मच तेल,1 कप गेहूं का आटा,नमक - स्वाद अनुसार,2 शकरकंदी - छीलकर उबाल ले,1 कप मेथी - काट ले,1 छोटा चम्मच जीरा,1 इंच अदरक - काट ले,1 हरी मिर्च - काट ले,1 छोटा चम्मच हल्दी पाउडर,1 छोटा चम्मच लाल मिर्च पाउडर,1 छोटा चम्मच धनिया पाउडर,2 बड़ा चम्मच सरसों का तेल,नमक - स्वाद अनुसार 6868 150 grams Spring Onion (Bulb & Greens) - chopped,3/4 cup Tamarind Water,1 tablespoon Gram flour (besan),2 teaspoons Sunflower Oil,1 teaspoon Mustard seeds,1 teaspoon Cumin seeds (Jeera),2 Dry Red Chillies,1 pinch Asafoetida (hing),1 pinch Turmeric powder (Haldi),Salt - to atste,2 teaspoons Coriander (Dhania) Seeds,1 teaspoon Cumin seeds (Jeera),1/2 teaspoon Mustard seeds,1/4 teaspoon Methi Seeds (Fenugreek Seeds),2 cups Water 6869 1 kg Chicken - medium pieces,1/2 cup Mustard oil,Ghee - as required,1-1/2 kg Curd (Dahi / Yogurt),2 inch Cinnamon Stick (Dalchini),2 to 3 Black cardamom (Badi Elaichi),3 to 4 Cloves (Laung),2 teaspoons Asafoetida (hing),2 Bay leaf (tej patta),1 teaspoon Cumin seeds (Jeera),1-1/2 teaspoons Fennel Powder,Salt - as required,1/2 cup Mint Leaves (Pudina) - dry 6870 2 बड़े चम्मच हरी मूंग दाल,2 बड़े चम्मच सफ़ेद उरद दाल,2 बड़े चम्मच काली उरद दाल (स्प्लिट),2 बड़े चम्मच मसूर दाल (स्प्लिट),2 बड़े चम्मच मसूर दाल,2 बड़े चम्मच राजमा,2 बड़े चम्मच काबुली चना या छोला,1/4 कप पिली मूंग दाल - 20 मिनट के लिए भिगो दे,1 बड़ा चम्मच अरहर दाल - 20 मिनट के लिए भिगो दे,1 प्याज - बारीक काट ले,1 टमाटर - बारीक काट ले,1 बड़ा चम्मच अदरक लहसुन का पेस्ट,1 हरी मिर्च - सीधा काट ले,2 छोटे चम्मच धनिया पाउडर,1 छोटा चम्मच लाल मिर्च पाउडर,2 छोटे चम्मच गरम मसाला पाउडर,1 बड़ा चम्मच कसूरी मेथी,1 इंच अदरक - सीधा और पतला काट ले,2 बड़े चम्मच घी,1 छोटा चम्मच जीरा,1 दालचीनी,नमक - स्वाद अनुसार Name: TranslatedIngredients, Length: 6871, dtype: object
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import re non_english_pattern = re.compile(r'[^\x00-\x7F]+') mask = df['TranslatedIngredients'].str.contains(non_english_pattern, na=False) df_english = df[~mask]
df_english.info()
<class 'pandas.core.frame.DataFrame'> Index: 6179 entries, 0 to 6869 Data columns (total 15 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 Srno 6179 non-null int64 1 RecipeName 6179 non-null object 2 TranslatedRecipeName 6179 non-null object 3 Ingredients 6173 non-null object 4 TranslatedIngredients 6173 non-null object 5 PrepTimeInMins 6179 non-null int64 6 CookTimeInMins 6179 non-null int64 7 TotalTimeInMins 6179 non-null int64 8 Servings 6179 non-null int64 9 Cuisine 6179 non-null object 10 Course 6179 non-null object 11 Diet 6179 non-null object 12 Instructions 6179 non-null object 13 TranslatedInstructions 6179 non-null object 14 URL 6179 non-null object dtypes: int64(5), object(10) memory usage: 772.4+ KB
df_english['TranslatedIngredients']
0 6 Karela (Bitter Gourd/ Pavakkai) - deseeded,Salt - to taste,1 Onion - thinly sliced,3 tablespoon Gram flour (besan),2 teaspoons Turmeric powder (Haldi),1 tablespoon Red Chilli powder,2 teaspoons Cumin seeds (Jeera),1 tablespoon Coriander Powder (Dhania),1 tablespoon Amchur (Dry Mango Powder),Sunflower Oil - as required 1 2-1 / 2 cups rice - cooked, 3 tomatoes, 3 teaspoons BC Belle Bhat powder, salt - as per taste, 1 teaspoon chickpea lentils, 1/2 teaspoon cumin seeds, 1 teaspoon white urad dal, 1/2 Teaspoon mustard, 1 green chilli, 1 dry red chilli, 2 teaspoon cashew - or peanuts, 1-1 / 2 tablespoon oil - 1/2 teaspoon asafoetida 2 1-1/2 cups Rice Vermicelli Noodles (Thin),1 Onion - sliced,1/2 cup Carrots (Gajjar) - chopped,1/3 cup Green peas (Matar),2 Green Chillies,1/4 teaspoon Asafoetida (hing),1 teaspoon Mustard seeds,1 teaspoon White Urad Dal (Split),1 teaspoon Ghee,1 sprig Curry leaves,Salt - to taste,1 teaspoon Lemon juice 3 500 grams Chicken,2 Onion - chopped,1 Tomato - chopped,4 Green Chillies - slit,1 inch Ginger - finely chopped,6 cloves Garlic - finely chopped,1/2 teaspoon Turmeric powder (Haldi),1 teaspoon Garam masala powder,2 tablespoon Sesame (Gingelly) Oil,Salt - to taste,1/4 teaspoon Methi Seeds (Fenugreek Seeds),1 tablespoon Coriander (Dhania) Seeds,4 Dry Red Chillies,1 teaspoon Fennel seeds (Saunf),1 teaspoon Sesame (Gingelly) Oil,4 cloves Garlic,2 cups Sorrel Leaves (Gongura) - picked and chopped,Salt - to taste 4 1 tablespoon chana dal, 1 tablespoon white urad dal, 2 red chillies, 1 tablespoon coriander seeds, 3 inches ginger - chop, 1 onion - chop, 1 tomato - chop, salt - as per taste, 1 Teaspoon mustard, asafoetida - a pinch, oil - as per use, 1 sprig curry ... 6863 2 cups Paneer (Homemade Cottage Cheese) - crumbled,4 tablespoons Sugar,1/2 teaspoon Cardamom Powder (Elaichi),Saffron strands - a generous pinch,2 tablespoons Water,1/2 teaspoon Ghee,2 teaspoons Rose water 6864 1-1/2 cup Risotto - cooked risotto (recipe below),1 cup Parmesan cheese - grated,1/2 cup Gorgonzola cheese - cut into 1/4 inch cubes,4 sprig Basil leaves - finely chopped,2 Whole Eggs,2 cups Whole Wheat Bread crumbs - Italian style seasoned,Homemade Pizza And Pasta Sauce - or Marinara Sauce for serving,1 cup Arborio rice,2 cups Vegetable stock - or chicken broth,1/4 cup Butter,1 Onion - finely chopped,1/2 cup Dry white wine,1/2 cup Parmesan cheese - grated,Salt - to taste,Black pepper powder - to taste 6865 1 cup Quinoa,3/4 cup Sugar,1 teaspoon Cardamom Powder (Elaichi),2 cup Milk - vegans can substitute this ingredient with almond milk,1/2 tablespoon Condensed Milk - or khoya (mawa) vegans can eliminate this ingredient,1/2 tablespoon Ghee,1/4 teaspoon Saffron strands,Mixed nuts - chopped 6868 150 grams Spring Onion (Bulb & Greens) - chopped,3/4 cup Tamarind Water,1 tablespoon Gram flour (besan),2 teaspoons Sunflower Oil,1 teaspoon Mustard seeds,1 teaspoon Cumin seeds (Jeera),2 Dry Red Chillies,1 pinch Asafoetida (hing),1 pinch Turmeric powder (Haldi),Salt - to atste,2 teaspoons Coriander (Dhania) Seeds,1 teaspoon Cumin seeds (Jeera),1/2 teaspoon Mustard seeds,1/4 teaspoon Methi Seeds (Fenugreek Seeds),2 cups Water 6869 1 kg Chicken - medium pieces,1/2 cup Mustard oil,Ghee - as required,1-1/2 kg Curd (Dahi / Yogurt),2 inch Cinnamon Stick (Dalchini),2 to 3 Black cardamom (Badi Elaichi),3 to 4 Cloves (Laung),2 teaspoons Asafoetida (hing),2 Bay leaf (tej patta),1 teaspoon Cumin seeds (Jeera),1-1/2 teaspoons Fennel Powder,Salt - as required,1/2 cup Mint Leaves (Pudina) - dry Name: TranslatedIngredients, Length: 6179, dtype: object
Now we had successfully removed the rows which were not in english
Next step is to separate the Ingredients by commas in separate column to respective recipe
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NLP for extracting ingredients
Natural Language Processing (NLP) techniques to extract the ingredients from the “TranslatedIngredients” column and create a new column with the ingredients separated by commas
import spacy nlp = spacy.load("en_core_web_sm")
def extract_ingredients(text): if pd.isnull(text): return "" doc = nlp(text) ingredients = [token.text for token in doc if token.pos_ in ['NOUN', 'PROPN'] or token.dep_ == 'compound'] return ', '.join(ingredients)
df_english['ExtractedIngredients'] = df_english['TranslatedIngredients'].apply(extract_ingredients)
df_english['ExtractedIngredients'].head()
0 Karela, Gourd/, Pavakkai, Salt, Onion, sliced,3, tablespoon, Gram, flour, besan),2, teaspoons, powder, tablespoon, Red, Chilli, powder,2, Cumin, seeds, Jeera),1, tablespoon, Coriander, Powder, Dhania),1, tablespoon, Amchur, Dry, Mango, Powder),Sunflower, Oil 1 cups, rice, tomatoes, teaspoons, BC, Belle, Bhat, powder, salt, taste, teaspoon, chickpea, lentils, teaspoon, cumin, seeds, teaspoon, white, urad, dal, Teaspoon, mustard, chilli, chilli, teaspoon, cashew, peanuts, tablespoon, oil, teaspoon, asafoetida 2 1, cups, Rice, Vermicelli, Noodles, Thin),1, Onion, sliced,1/2, cup, Carrots, Gajjar, chopped,1/3, cup, Green, peas, Matar),2, Green, Chillies,1/4, teaspoon, Asafoetida, hing),1, teaspoon, Mustard, seeds,1, teaspoon, White, Urad, Dal, teaspoon, Ghee,1, sprig, Curry, Salt, teaspoon, Lemon, juice 3 grams, Chicken,2, Onion, chopped,1, Tomato, chopped,4, Green, Chillies, slit,1, inch, Ginger, cloves, Garlic, chopped,1/2, teaspoon, Turmeric, powder, Haldi),1, teaspoon, Garam, masala, powder,2, tablespoon, Sesame, Gingelly, Oil, Salt, teaspoon, Methi, Seeds, Fenugreek, Seeds),1, tablespoon, Coriander, Dhania, Seeds,4, Dry, Red, Chillies,1, teaspoon, Fennel, seeds, Saunf),1, teaspoon, Sesame, Gingelly, Oil,4, Garlic,2, Sorrel, Leaves, Gongura, Salt 4 tablespoon, chana, dal, tablespoon, white, urad, dal, chillies, tablespoon, coriander, seeds, inches, ginger, chop, onion, chop, tomato, chop, salt, taste, Teaspoon, mustard, asafoetida, pinch, oil, use, sprig, curry Name: ExtractedIngredients, dtype: object
We have extracted ingredients but some numbers, brackets, etc is still in the information, so we will try to remove that too
def clean_ingredients(text): text = re.sub(r'\d+', '', text) text = re.sub(r'[()\[\]/]', '', text) return text
df_english['ExtractedIngredients'] = df_english['ExtractedIngredients'].apply(clean_ingredients)
df_english['ExtractedIngredients']
0 Karela, Gourd, Pavakkai, Salt, Onion, sliced,, tablespoon, Gram, flour, besan,, teaspoons, powder, tablespoon, Red, Chilli, powder,, Cumin, seeds, Jeera,, tablespoon, Coriander, Powder, Dhania,, tablespoon, Amchur, Dry, Mango, Powder,Sunflower, Oil 1 cups, rice, tomatoes, teaspoons, BC, Belle, Bhat, powder, salt, taste, teaspoon, chickpea, lentils, teaspoon, cumin, seeds, teaspoon, white, urad, dal, Teaspoon, mustard, chilli, chilli, teaspoon, cashew, peanuts, tablespoon, oil, teaspoon, asafoetida 2 , cups, Rice, Vermicelli, Noodles, Thin,, Onion, sliced,, cup, Carrots, Gajjar, chopped,, cup, Green, peas, Matar,, Green, Chillies,, teaspoon, Asafoetida, hing,, teaspoon, Mustard, seeds,, teaspoon, White, Urad, Dal, teaspoon, Ghee,, sprig, Curry, Salt, teaspoon, Lemon, juice 3 grams, Chicken,, Onion, chopped,, Tomato, chopped,, Green, Chillies, slit,, inch, Ginger, cloves, Garlic, chopped,, teaspoon, Turmeric, powder, Haldi,, teaspoon, Garam, masala, powder,, tablespoon, Sesame, Gingelly, Oil, Salt, teaspoon, Methi, Seeds, Fenugreek, Seeds,, tablespoon, Coriander, Dhania, Seeds,, Dry, Red, Chillies,, teaspoon, Fennel, seeds, Saunf,, teaspoon, Sesame, Gingelly, Oil,, Garlic,, Sorrel, Leaves, Gongura, Salt 4 tablespoon, chana, dal, tablespoon, white, urad, dal, chillies, tablespoon, coriander, seeds, inches, ginger, chop, onion, chop, tomato, chop, salt, taste, Teaspoon, mustard, asafoetida, pinch, oil, use, sprig, curry ... 6863 cups, Paneer, Homemade, Cottage, Cheese, crumbled,, tablespoons, Sugar,, teaspoon, Cardamom, Powder, Elaichi,Saffron, strands, pinch,, tablespoons, Water,, teaspoon, Ghee,, Rose, water 6864 cup, Risotto, risotto, recipe, below,, cup, Parmesan, cheese, cup, Gorgonzola, cheese, inch, cubes,, sprig, Basil, Eggs,, cups, Whole, Wheat, Bread, style, Homemade, Pizza, Pasta, Sauce, Marinara, Sauce, serving,, cup, Arborio, cups, Vegetable, stock, chicken, broth,, cup, Butter,, Onion, chopped,, cup, Dry, white, wine,, cup, Parmesan, cheese, Salt, pepper, powder 6865 cup, Quinoa,, cup, Sugar,, teaspoon, Cardamom, Powder, Elaichi,, cup, Milk, vegans, ingredient, milk,, tablespoon, Milk, khoya, mawa, vegans, ingredient,, tablespoon, Ghee,, teaspoon, Saffron, strands, Mixed, nuts 6868 grams, Spring, Onion, Bulb, Greens, chopped,, cup, Tamarind, Water,, tablespoon, Gram, flour, besan,, teaspoons, Sunflower, teaspoon, Mustard, seeds,, teaspoon, Cumin, seeds, Jeera,, Dry, Red, Chillies,, Asafoetida, powder, Haldi,Salt, teaspoons, Coriander, Dhania, Seeds,, teaspoon, Cumin, seeds, Jeera,, teaspoon, Mustard, seeds,, teaspoon, Methi, Seeds, Fenugreek, Seeds,, Water 6869 kg, Chicken, medium, pieces,, cup, Mustard, oil, Ghee, required,, kg, Curd, Dahi, Yogurt,, inch, Cinnamon, Stick, Dalchini,, cardamom, Badi, Elaichi,, Cloves, Laung,, Asafoetida, Bay, leaf, tej, patta,, teaspoon, Cumin, seeds, Jeera,, teaspoons, Fennel, Powder, Salt, required,, cup, Mint, Leaves, Pudina Name: ExtractedIngredients, Length: 6179, dtype: object
df_english.info()
<class 'pandas.core.frame.DataFrame'> Index: 6179 entries, 0 to 6869 Data columns (total 16 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 Srno 6179 non-null int64 1 RecipeName 6179 non-null object 2 TranslatedRecipeName 6179 non-null object 3 Ingredients 6173 non-null object 4 TranslatedIngredients 6173 non-null object 5 PrepTimeInMins 6179 non-null int64 6 CookTimeInMins 6179 non-null int64 7 TotalTimeInMins 6179 non-null int64 8 Servings 6179 non-null int64 9 Cuisine 6179 non-null object 10 Course 6179 non-null object 11 Diet 6179 non-null object 12 Instructions 6179 non-null object 13 TranslatedInstructions 6179 non-null object 14 URL 6179 non-null object 15 ExtractedIngredients 6179 non-null object dtypes: int64(5), object(11) memory usage: 820.6+ KB
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Recommendation System
- Text Preprocessing: Perform text preprocessing steps such as lowercasing, removing punctuation, and tokenizing the ingredients.
- Term Frequency-Inverse Document Frequency (TF-IDF): Use TF-IDF vectorization to represent ingredients in numerical form, giving more weight to ingredients that are rare across recipes but common within a recipe.
- Cosine Similarity with Weighted Features: Calculate cosine similarity between the user-provided ingredients and recipe ingredients, but also consider other features such as preparation time, cooking time, and total time. Weight these features accordingly to balance their importance in the similarity calculation.
- Ranking with Combined Scores: Combine the similarity scores from ingredient vectors and other features, then rank the recipes based on the combined scores.
import string import nltk from nltk.corpus import stopwords from sklearn.feature_extraction.text import TfidfVectorizer from sklearn.metrics.pairwise import cosine_similarity
nltk.download('stopwords')
[nltk_data] Downloading package stopwords to /usr/share/nltk_data... [nltk_data] Package stopwords is already up-to-date!
True
Text Preprocessing
def preprocess_text(text): # Lowercase the text text = text.lower() # Remove punctuation text = ''.join([char for char in text if char not in string.punctuation]) # Tokenize the text tokens = nltk.word_tokenize(text) # Remove stopwords stop_words = set(stopwords.words('english')) # Explicitly load stopwords from NLTK corpus tokens = [word for word in tokens if word not in stop_words] return ' '.join(tokens)
df_english['CleanedIngredients'] = df_english['ExtractedIngredients'].apply(preprocess_text)
df_english['CleanedIngredients'].head()
0 karela gourd pavakkai salt onion sliced tablespoon gram flour besan teaspoons powder tablespoon red chilli powder cumin seeds jeera tablespoon coriander powder dhania tablespoon amchur dry mango powdersunflower oil 1 cups rice tomatoes teaspoons bc belle bhat powder salt taste teaspoon chickpea lentils teaspoon cumin seeds teaspoon white urad dal teaspoon mustard chilli chilli teaspoon cashew peanuts tablespoon oil teaspoon asafoetida 2 cups rice vermicelli noodles thin onion sliced cup carrots gajjar chopped cup green peas matar green chillies teaspoon asafoetida hing teaspoon mustard seeds teaspoon white urad dal teaspoon ghee sprig curry salt teaspoon lemon juice 3 grams chicken onion chopped tomato chopped green chillies slit inch ginger cloves garlic chopped teaspoon turmeric powder haldi teaspoon garam masala powder tablespoon sesame gingelly oil salt teaspoon methi seeds fenugreek seeds tablespoon coriander dhania seeds dry red chillies teaspoon fennel seeds saunf teaspoon sesame gingelly oil garlic sorrel leaves gongura salt 4 tablespoon chana dal tablespoon white urad dal chillies tablespoon coriander seeds inches ginger chop onion chop tomato chop salt taste teaspoon mustard asafoetida pinch oil use sprig curry Name: CleanedIngredients, dtype: object
TF-IDF Vectorization
tfidf_vectorizer = TfidfVectorizer() tfidf_matrix = tfidf_vectorizer.fit_transform(df_english['CleanedIngredients'])
Calculate Similarity with Weighted Features
def calculate_similarity(user_ingredients, user_prep_time, user_cook_time): # User ingredients user_ingredients_text = preprocess_text(', '.join(user_ingredients)) user_tfidf = tfidf_vectorizer.transform([user_ingredients_text]) cosine_similarities = cosine_similarity(user_tfidf, tfidf_matrix)[0] # Weighted features prep_time_similarity = 1 - abs(df['PrepTimeInMins'] - user_prep_time) / df['PrepTimeInMins'].max() cook_time_similarity = 1 - abs(df['CookTimeInMins'] - user_cook_time) / df['CookTimeInMins'].max() # Align shapes min_length = min(len(cosine_similarities), len(prep_time_similarity), len(cook_time_similarity)) cosine_similarities = cosine_similarities[:min_length] prep_time_similarity = prep_time_similarity[:min_length] cook_time_similarity = cook_time_similarity[:min_length] # Combine similarity scores with weighted features combined_similarity = (cosine_similarities + prep_time_similarity + cook_time_similarity) / 3 return combined_similarity
Ranking with Combined Scores
def recommend_recipes(user_ingredients, user_prep_time, user_cook_time, top_n=5): combined_similarity = calculate_similarity(user_ingredients, user_prep_time, user_cook_time) # print("Shape of combined_similarity:", combined_similarity.shape) # Sort the indices based on similarity scores in descending order sorted_indices = combined_similarity.argsort()[::-1] # print("Sorted indices:", sorted_indices) # print("Top n:", top_n) # print("Column indexer:", df.columns.get_indexer(['RecipeName', 'TranslatedRecipeName'])) # Select top_n recipes based on sorted indices top_recommendations = df.iloc[sorted_indices[:top_n], df.columns.get_indexer(['RecipeName', 'TranslatedRecipeName'])].copy() return top_recommendations
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Example Use
user_ingredients = ["onion", "tomato", "garlic", "ginger"] user_prep_time = 30 # minutes user_cook_time = 45 # minutes recommendations = recommend_recipes(user_ingredients, user_prep_time, user_cook_time) recommendations
RecipeName | TranslatedRecipeName | |
---|---|---|
2516 | Broccoli & Vegetable Coconut Curry Recipe | Broccoli & Vegetable Coconut Curry Recipe |
2080 | Andhra Style Makka Garelu Recipe (Corn Vada Recipe) | Andhra Style Makka Garelu Recipe (Corn Vada Recipe) |
5721 | Vermicelli Kunafa Recipe With Paneer & Cream Filling | Vermicelli Kunafa Recipe With Paneer & Cream Filling |
5351 | Baked Cheesy Broccoli Pasta Recipe | Baked Cheesy Broccoli Pasta Recipe |
144 | कटाची आमटी रेसिपी – Katachi Amti (Recipe In Hindi) | Katachi Amti (Recipe In Hindi) |
Print whole recipe
for index in recommendations.index: recipe_info = df.loc[index] print("Recipe Information for Index", index) print(recipe_info) print("---------------------------------------------------------------------------------------------")
Recipe Information for Index 2516 Srno 2964 RecipeName Broccoli & Vegetable Coconut Curry Recipe TranslatedRecipeName Broccoli & Vegetable Coconut Curry Recipe Ingredients 400 grams Broccoli - cut into florets,1 Red Bell pepper (Capsicum) - thinly sliced,1 Onion - chopped,4 Dry Red Chilli,1 teaspoon Methi Seeds (Fenugreek Seeds),1 tablespoon Coriander (Dhania) Seeds,1 teaspoon Cumin seeds (Jeera),1 tablespoon Whole Black Peppercorns,Tamarind - lemon size,2 cups Fresh coconut - grated,3 cloves Garlic,1 teaspoon Turmeric powder (Haldi),Salt - to taste,Sunflower Oil - for cooking TranslatedIngredients 400 grams Broccoli - cut into florets,1 Red Bell pepper (Capsicum) - thinly sliced,1 Onion - chopped,4 Dry Red Chilli,1 teaspoon Methi Seeds (Fenugreek Seeds),1 tablespoon Coriander (Dhania) Seeds,1 teaspoon Cumin seeds (Jeera),1 tablespoon Whole Black Peppercorns,Tamarind - lemon size,2 cups Fresh coconut - grated,3 cloves Garlic,1 teaspoon Turmeric powder (Haldi),Salt - to taste,Sunflower Oil - for cooking PrepTimeInMins 20 CookTimeInMins 40 TotalTimeInMins 60 Servings 4 Cuisine Mangalorean Course Side Dish Diet Diabetic Friendly Instructions To begin making the Broccoli & Vegetable Coconut Curry Recipe, we will first make the Broccoli Curry Masala. In a small pan; roast the dry red chillies, fenugreek, coriander, cumin and black pepper until you can smell the roasted aromas. Add these to a small blender jar, along with the coconut, tamarind and turmeric powder. Grind the spices adding very little water to make a smooth curry paste. The curry paste is now ready. Keep aside.In the next step; heat a teaspoon of oil in a pan; add the chopped onions, broccoli and red bell peppers and saute for a couple of minutes until the onions are soft and tender. Add the curry paste and saute for a couple of more minutes. Stir in salt and add about 1-1/2 to 2 cups of water. You essentially want a thick gravy for the Broccoli Curry; hence add water accordingly. Give all the ingredients a good stir. Check the salt levels and adjust to suit your taste. Give the mixture a quick boil only when you are ready to serve. Serve the Broccoli & Vegetable Coconut Curry, along with Neer Dosa or Kerala (Lachha) Paratha or even plain steamed rice.\n TranslatedInstructions To begin making the Broccoli & Vegetable Coconut Curry Recipe, we will first make the Broccoli Curry Masala. In a small pan; roast the dry red chillies, fenugreek, coriander, cumin and black pepper until you can smell the roasted aromas. Add these to a small blender jar, along with the coconut, tamarind and turmeric powder. Grind the spices adding very little water to make a smooth curry paste. The curry paste is now ready. Keep aside.In the next step; heat a teaspoon of oil in a pan; add the chopped onions, broccoli and red bell peppers and saute for a couple of minutes until the onions are soft and tender. Add the curry paste and saute for a couple of more minutes. Stir in salt and add about 1-1/2 to 2 cups of water. You essentially want a thick gravy for the Broccoli Curry; hence add water accordingly. Give all the ingredients a good stir. Check the salt levels and adjust to suit your taste. Give the mixture a quick boil only when you are ready to serve. Serve the Broccoli & Vegetable Coconut Curry, along with Neer Dosa or Kerala (Lachha) Paratha or even plain steamed rice. URL https://www.archanaskitchen.com/broccoli-and-vegetable-curry-recipe Name: 2516, dtype: object --------------------------------------------------------------------------------------------- Recipe Information for Index 2080 Srno 2399 RecipeName Andhra Style Makka Garelu Recipe (Corn Vada Recipe) TranslatedRecipeName Andhra Style Makka Garelu Recipe (Corn Vada Recipe) Ingredients 1 cup Sweet corn,1/4 cup Gram flour (besan),1 Onion - chopped,2 Green Chillies,1 sprig Curry leaves,4 cloves Garlic,1 teaspoon Kalonji (Onion Nigella Seeds),Salt - to taste,Sunflower Oil TranslatedIngredients 1 cup Sweet corn,1/4 cup Gram flour (besan),1 Onion - chopped,2 Green Chillies,1 sprig Curry leaves,4 cloves Garlic,1 teaspoon Kalonji (Onion Nigella Seeds),Salt - to taste,Sunflower Oil PrepTimeInMins 10 CookTimeInMins 30 TotalTimeInMins 40 Servings 4 Cuisine Andhra Course Snack Diet Vegetarian Instructions To begin making the Andhra Style Makka Garelu Recipe, blitz the corn in a mixer without water to a very chunky mixture.Transfer to a bowl, add the remaining ingredients including the besan and combine to make a thick garelu mixture. Check the salt and adjust to taste accordingly.Heat oil for deep frying. Once the oil is hot take small portions of the makka garelu mixture, flatten it to make a round disc and slide into the hot oil.Fry until the garelu is browned and crisp. Ensure you fry over medium heat, so it gets cooked evenly from inside out.Serve the Makka Garelu along with hot tea or dip it with a Date and Tamarind Chutney Recipe to enjoy your evening snack. \n TranslatedInstructions To begin making the Andhra Style Makka Garelu Recipe, blitz the corn in a mixer without water to a very chunky mixture.Transfer to a bowl, add the remaining ingredients including the besan and combine to make a thick garelu mixture. Check the salt and adjust to taste accordingly.Heat oil for deep frying. Once the oil is hot take small portions of the makka garelu mixture, flatten it to make a round disc and slide into the hot oil.Fry until the garelu is browned and crisp. Ensure you fry over medium heat, so it gets cooked evenly from inside out.Serve the Makka Garelu along with hot tea or dip it with a Date and Tamarind Chutney Recipe to enjoy your evening snack. \n URL http://www.archanaskitchen.com/andhra-style-makka-garelu-recipe-maize-kernel-vada-recipe Name: 2080, dtype: object --------------------------------------------------------------------------------------------- Recipe Information for Index 5721 Srno 8717 RecipeName Vermicelli Kunafa Recipe With Paneer & Cream Filling TranslatedRecipeName Vermicelli Kunafa Recipe With Paneer & Cream Filling Ingredients 100 grams Semiya (Vermicelli) - (thin variety),25 grams Butter - melted,1-1/2 cups Milk,50 grams Paneer (Homemade Cottage Cheese),1/4 tablespoon Corn flour,1/4 tablespoon Custard powder,1/2 tablespoon All Purpose Flour (Maida),1-1/2 tablespoons Condensed Milk,2 tablespoons Sugar,2 Cardamom (Elaichi) Pods/Seeds - ground,1/8 teaspoon Almond essence (Badam Essence),1/8 teaspoon Kewra essence,1/4 cup Mixed nuts - (I used Almond,1 cup Water,3 tablespoons Sugar,3 tablespoons Honey,1/2 teaspoon Rose water,1 tablespoon Lemon juice TranslatedIngredients 100 grams Semiya (Vermicelli) - (thin variety),25 grams Butter - melted,1-1/2 cups Milk,50 grams Paneer (Homemade Cottage Cheese),1/4 tablespoon Corn flour,1/4 tablespoon Custard powder,1/2 tablespoon All Purpose Flour (Maida),1-1/2 tablespoons Condensed Milk,2 tablespoons Sugar,2 Cardamom (Elaichi) Pods/Seeds - ground,1/8 teaspoon Almond essence (Badam Essence),1/8 teaspoon Kewra essence,1/4 cup Mixed nuts - (I used Almond,1 cup Water,3 tablespoons Sugar,3 tablespoons Honey,1/2 teaspoon Rose water,1 tablespoon Lemon juice PrepTimeInMins 30 CookTimeInMins 45 TotalTimeInMins 75 Servings 6 Cuisine Fusion Course Dessert Diet Vegetarian Instructions For layering:To begin making Vermicelli Kunafa Recipe With Paneer & Cheese Cream Filling, start by preparing the layering. Break the vermicelli with fingers in a large bowl and add butter to it. Reserve some of it to roast the nuts later and greasing the baking tray.Now grease a baking tray with butter, always grease upward (I used silicon mold as it is easy to demold). Here you can use silicone cups as well where you can take out individual servings.Now place the vermicelli in the baking tray, press well with fingers, making around 1 inch high layer at the bottom.Preheat the oven on 200 degrees celsius.For Filling:Heat a deep sauce pan over low heat and pour milk into it.Add grated paneer to it.Now mix corn flour, custard powder & maida in some cold water. Mix well till so that there are no lumps in the mixture.Now pour the mixture in the saucepan with boiling milk and continue boiling with constant stirring with a wooden spatula.Now add sugar to it, and continue stirring. Add Almond extract & Kewra, stirring constantly till it gets a thick creamy textureNow add condensed milk, cardamom powder and switch off heat. Let it cool for some time.Assembling:For syrup, mix all the ingredients mentioned for syrup together in a saucepan & boil for 5-7 minutes or till thickened considerably, and switch off.Also in a small pan, heat butter and roast the chopped nuts till fragrant, on low heat such that the nuts aren't burnt.Now pour the paneer mixture over the set vermicelli. Top the remaining vermicelli over the paneer mixture lightly, don’t press the vermicelli too much.Now bake for 45-50 minutes or till the the top layer of vermicelli becomes golden. Take care not to burn the vermicelli. Keep checking after 20 minutes in oven, since every oven has a different heating temperature.After it gets baked, take out the tray & let it rest for 5 mins then de-mold the cake cautiously, running a knife along the edges.Garnish with the roasted nuts. Reserve half of them for later.Now pour the prepared thickened honey syrup.Garnish with the remaining roasted nuts.Serve Vermicelli Kunafa Recipe With Paneer & Cheese Cream Filling as a festive snack when your relatives and guest visit you during this festive season. You can serve it along with Sattu Ka Namkeen Sharbat Recipe.\n TranslatedInstructions For layering:To begin making Vermicelli Kunafa Recipe With Paneer & Cheese Cream Filling, start by preparing the layering. Break the vermicelli with fingers in a large bowl and add butter to it. Reserve some of it to roast the nuts later and greasing the baking tray.Now grease a baking tray with butter, always grease upward (I used silicon mold as it is easy to demold). Here you can use silicone cups as well where you can take out individual servings.Now place the vermicelli in the baking tray, press well with fingers, making around 1 inch high layer at the bottom.Preheat the oven on 200 degrees celsius.For Filling:Heat a deep sauce pan over low heat and pour milk into it.Add grated paneer to it.Now mix corn flour, custard powder & maida in some cold water. Mix well till so that there are no lumps in the mixture.Now pour the mixture in the saucepan with boiling milk and continue boiling with constant stirring with a wooden spatula.Now add sugar to it, and continue stirring. Add Almond extract & Kewra, stirring constantly till it gets a thick creamy textureNow add condensed milk, cardamom powder and switch off heat. Let it cool for some time.Assembling:For syrup, mix all the ingredients mentioned for syrup together in a saucepan & boil for 5-7 minutes or till thickened considerably, and switch off.Also in a small pan, heat butter and roast the chopped nuts till fragrant, on low heat such that the nuts aren't burnt.Now pour the paneer mixture over the set vermicelli. Top the remaining vermicelli over the paneer mixture lightly, don’t press the vermicelli too much.Now bake for 45-50 minutes or till the the top layer of vermicelli becomes golden. Take care not to burn the vermicelli. Keep checking after 20 minutes in oven, since every oven has a different heating temperature.After it gets baked, take out the tray & let it rest for 5 mins then de-mold the cake cautiously, running a knife along the edges.Garnish with the roasted nuts. Reserve half of them for later.Now pour the prepared thickened honey syrup.Garnish with the remaining roasted nuts.Serve Vermicelli Kunafa Recipe With Paneer & Cheese Cream Filling as a festive snack when your relatives and guest visit you during this festive season. You can serve it along with Sattu Ka Namkeen Sharbat Recipe.\n URL http://www.archanaskitchen.com/vermicelli-kunafa-recipe-with-paneer-cream-filling Name: 5721, dtype: object --------------------------------------------------------------------------------------------- Recipe Information for Index 5351 Srno 7803 RecipeName Baked Cheesy Broccoli Pasta Recipe TranslatedRecipeName Baked Cheesy Broccoli Pasta Recipe Ingredients 350 grams Penne pasta,2 cups Broccoli - florets,6 cloves Garlic - finely chopped,1/2 teaspoon Black pepper powder,2 teaspoons Italian seasoning,1 teaspoon Red Chilli flakes,1/4 cup Mozzarella cheese - grated,1 tablespoon Extra Virgin Olive Oil,Salt - to taste,2 tablespoons Whole Wheat Flour,1 cup Milk,1/4 cup Fresh cream,Salt and Pepper - to taste,1/4 teaspoon Nutmeg TranslatedIngredients 350 grams Penne pasta,2 cups Broccoli - florets,6 cloves Garlic - finely chopped,1/2 teaspoon Black pepper powder,2 teaspoons Italian seasoning,1 teaspoon Red Chilli flakes,1/4 cup Mozzarella cheese - grated,1 tablespoon Extra Virgin Olive Oil,Salt - to taste,2 tablespoons Whole Wheat Flour,1 cup Milk,1/4 cup Fresh cream,Salt and Pepper - to taste,1/4 teaspoon Nutmeg PrepTimeInMins 10 CookTimeInMins 30 TotalTimeInMins 40 Servings 4 Cuisine Italian Recipes Course Dinner Diet High Protein Vegetarian Instructions To begin making the Broccoli Cheesy Pesto Pasta Recipe, first get all the ingredients ready.To Cook the Pasta Keep 500ml of water in a stock pot and allow it to boil, add in pasta along with a teaspoon of salt and allow it to boil and cook for at least 10-15 minutes until the pasta is cooked till al dente. Once the pasta is cooked,drain the water and rinse it under cold water to prevent it from getting overcooked in the heat. Drizzle some olive oil over the pasta and toss it well and keep the cooked pasta aside.To Make the Bechamel Sauce Whisk together the flours, salt, pepper, nutmeg and the milk until all the flour is well dissolved into the milk. Pour the white sauce mixture into a saucepan and begin cooking the Bechamel sauce until it thickens.As the White Sauce pasta mixture-Bechamel Sauce begins to thicken, add in the cream and continue to stir well until there are no lumps and the mixture is cooked through completely.Once the sauce is cooked and you are happy with the consistency, switch off the flame. To Make the Baked Cheesy Broccoli Pasta Preheat the oven to 180 degree celsius for 10 minutes and keep it ready. Heat a teaspoon of oil in a wok, add chopped garlic and saute till it softens. Add in the broccoli florets, teaspoon of salt, and roast them until cooked through. Sprinkle the italian seasonings, red chilli flakes and pepper powder and give it a stir. Add in your White sauce-Bechamel sauce along with pasta and mix well until the pasta is well coated.Check the salt and adjust to suit your taste.Turn off the heat and transfer the Broccoli Pasta to a ceramic baked pan, sprinkle with mozzarella cheese on top and bake in the oven till the cheese melts. Serve the Broccoli Cheesy Pesto Pasta Recipe along with a Watermelon Panzanella Salad and glass of wine for a perfect weeknight dinner with your partner or even pack into a lunchbox for kids.\n TranslatedInstructions To begin making the Broccoli Cheesy Pesto Pasta Recipe, first get all the ingredients ready.To Cook the Pasta Keep 500ml of water in a stock pot and allow it to boil, add in pasta along with a teaspoon of salt and allow it to boil and cook for at least 10-15 minutes until the pasta is cooked till al dente. Once the pasta is cooked,drain the water and rinse it under cold water to prevent it from getting overcooked in the heat. Drizzle some olive oil over the pasta and toss it well and keep the cooked pasta aside.To Make the Bechamel Sauce Whisk together the flours, salt, pepper, nutmeg and the milk until all the flour is well dissolved into the milk. Pour the white sauce mixture into a saucepan and begin cooking the Bechamel sauce until it thickens.As the White Sauce pasta mixture-Bechamel Sauce begins to thicken, add in the cream and continue to stir well until there are no lumps and the mixture is cooked through completely.Once the sauce is cooked and you are happy with the consistency, switch off the flame. To Make the Baked Cheesy Broccoli Pasta Preheat the oven to 180 degree celsius for 10 minutes and keep it ready. Heat a teaspoon of oil in a wok, add chopped garlic and saute till it softens. Add in the broccoli florets, teaspoon of salt, and roast them until cooked through. Sprinkle the italian seasonings, red chilli flakes and pepper powder and give it a stir. Add in your White sauce-Bechamel sauce along with pasta and mix well until the pasta is well coated.Check the salt and adjust to suit your taste.Turn off the heat and transfer the Broccoli Pasta to a ceramic baked pan, sprinkle with mozzarella cheese on top and bake in the oven till the cheese melts. Serve the Broccoli Cheesy Pesto Pasta Recipe along with a Watermelon Panzanella Salad and glass of wine for a perfect weeknight dinner with your partner or even pack into a lunchbox for kids. URL http://www.archanaskitchen.com/broccoli-pesto-pasta-recipe Name: 5351, dtype: object --------------------------------------------------------------------------------------------- Recipe Information for Index 144 Srno 155 RecipeName कटाची आमटी रेसिपी - Katachi Amti (Recipe In Hindi) TranslatedRecipeName Katachi Amti (Recipe In Hindi) Ingredients 2 कप दाल का पानी - चना दाल का पानी,1 कप पानी - प्रयोग अनुसार,3 लॉन्ग,2 पूरी काली मिर्च,1 छोटा चमच्च जीरा,1 छोटा चमच्च राइ,1 तेज पत्ता,5 कढ़ी पत्ता - तोड़ ले,1 छोटा चमच्च गुड़ - कस ले,1 छोटा चमच्च इमली का पेस्ट - अपने अनुसार ज्यादा कम कर ले,1/2 छोटा चमच्च गोडा मसाला,1 बड़ा चमच्च तेल,हरा धनिया - थोड़ा,नमक - to taste TranslatedIngredients 2 cups lentil water - chana lentil water, 1 cup water - as per the experiment, 3 long, 2 whole black peppers, 1 teaspoon cumin, 1 teaspoon mustard, 1 bay leaf, 5 curry leaves - break, 1 teaspoon Jaggery - grated, 1 teaspoon tamarind paste - reduce as much as you like, 1/2 teaspoon goda masala, 1 tablespoon oil, coriander leaves - little, salt - to taste PrepTimeInMins 10 CookTimeInMins 15 TotalTimeInMins 25 Servings 4 Cuisine Maharashtrian Recipes Course Lunch Diet Vegetarian Instructions कटाची आमटी बनाने के लिए सबसे पहले पानी को दाल के पानी में मिला ले. अलग से रख दे. अब एक सॉसपैन में तेल गरम करें। इसमें राइ डाले और 10 सेकण्ड्स तक पकने दे. 10 सेकण्ड्स के बाद ,इसमें लॉन्ग, तेज पत्ता और काली मिर्च डाले। 20 सेकण्ड्स तक पकाने के बाद इसमें जीरा, कढ़ी पत्ता और हरा धनिया डाले। 30 सेकण्ड्स बाद, दाल का पानी, गुड़, इमली का पेस्ट, गोडा मसाला और नमक डाले। उबाला आने तक पकाए। उबाला आने के बाद गैस बंद करें और परोसे। कटाची आमटी को पुरन पोली के साथ त्यौहार के दिनों में परोसे।\n TranslatedInstructions To make kachchi mango, first mix the water in the lentils. Keep it aside. Now heat the oil in a saucepan. Add mustard seeds and let it cook for 10 seconds. After 10 seconds, add long, bay leaves and black pepper. After cooking for 20 seconds, add cumin seeds, curry leaves and coriander leaves. After 30 seconds, add lentil water, jaggery, tamarind paste, goda masala and salt. Cook until it boils. Turn off the gas after boiling and serve. Serve Katachi Amati with Puran Poli on festival days. URL https://www.archanaskitchen.com/katachi-amti-recipe-spicy-accompaniment-to-maharashtrian-puran-poli-in-hindi Name: 144, dtype: object ---------------------------------------------------------------------------------------------
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html_table = "<table><tr><th>Srno</th><th>Recipe Name</th><th>Translated Recipe Name</th><th>Ingredients</th><th>Translated Ingredients</th><th>Prep Time (mins)</th><th>Cook Time (mins)</th><th>Total Time (mins)</th><th>Servings</th><th>Cuisine</th><th>Course</th><th>Diet</th><th>Instructions</th><th>Translated Instructions</th><th>URL</th></tr>" for index in recommendations.index: recipe_info = df.loc[index] html_table += f"<tr><td>{recipe_info['Srno']}</td><td>{recipe_info['RecipeName']}</td><td>{recipe_info['TranslatedRecipeName']}</td><td>{recipe_info['Ingredients']}</td><td>{recipe_info['TranslatedIngredients']}</td><td>{recipe_info['PrepTimeInMins']}</td><td>{recipe_info['CookTimeInMins']}</td><td>{recipe_info['TotalTimeInMins']}</td><td>{recipe_info['Servings']}</td><td>{recipe_info['Cuisine']}</td><td>{recipe_info['Course']}</td><td>{recipe_info['Diet']}</td><td>{recipe_info['Instructions']}</td><td>{recipe_info['TranslatedInstructions']}</td><td>{recipe_info['URL']}</td></tr>" html_table += "</table>" HTML(html_table)
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Srno | Recipe Name | Translated Recipe Name | Ingredients | Translated Ingredients | Prep Time (mins) | Cook Time (mins) | Total Time (mins) | Servings | Cuisine | Course | Diet | Instructions | Translated Instructions | URL |
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2964 | Broccoli & Vegetable Coconut Curry Recipe | Broccoli & Vegetable Coconut Curry Recipe | 400 grams Broccoli – cut into florets,1 Red Bell pepper (Capsicum) – thinly sliced,1 Onion – chopped,4 Dry Red Chilli,1 teaspoon Methi Seeds (Fenugreek Seeds),1 tablespoon Coriander (Dhania) Seeds,1 teaspoon Cumin seeds (Jeera),1 tablespoon Whole Black Peppercorns,Tamarind – lemon size,2 cups Fresh coconut – grated,3 cloves Garlic,1 teaspoon Turmeric powder (Haldi),Salt – to taste,Sunflower Oil – for cooking | 400 grams Broccoli – cut into florets,1 Red Bell pepper (Capsicum) – thinly sliced,1 Onion – chopped,4 Dry Red Chilli,1 teaspoon Methi Seeds (Fenugreek Seeds),1 tablespoon Coriander (Dhania) Seeds,1 teaspoon Cumin seeds (Jeera),1 tablespoon Whole Black Peppercorns,Tamarind – lemon size,2 cups Fresh coconut – grated,3 cloves Garlic,1 teaspoon Turmeric powder (Haldi),Salt – to taste,Sunflower Oil – for cooking | 20 | 40 | 60 | 4 | Mangalorean | Side Dish | Diabetic Friendly | To begin making the Broccoli & Vegetable Coconut Curry Recipe, we will first make the Broccoli Curry Masala. In a small pan; roast the dry red chillies, fenugreek, coriander, cumin and black pepper until you can smell the roasted aromas. Add these to a small blender jar, along with the coconut, tamarind and turmeric powder. Grind the spices adding very little water to make a smooth curry paste. The curry paste is now ready. Keep aside.In the next step; heat a teaspoon of oil in a pan; add the chopped onions, broccoli and red bell peppers and saute for a couple of minutes until the onions are soft and tender. Add the curry paste and saute for a couple of more minutes. Stir in salt and add about 1-1/2 to 2 cups of water. You essentially want a thick gravy for the Broccoli Curry; hence add water accordingly. Give all the ingredients a good stir. Check the salt levels and adjust to suit your taste. Give the mixture a quick boil only when you are ready to serve. Serve the Broccoli & Vegetable Coconut Curry, along with Neer Dosa or Kerala (Lachha) Paratha or even plain steamed rice. | To begin making the Broccoli & Vegetable Coconut Curry Recipe, we will first make the Broccoli Curry Masala. In a small pan; roast the dry red chillies, fenugreek, coriander, cumin and black pepper until you can smell the roasted aromas. Add these to a small blender jar, along with the coconut, tamarind and turmeric powder. Grind the spices adding very little water to make a smooth curry paste. The curry paste is now ready. Keep aside.In the next step; heat a teaspoon of oil in a pan; add the chopped onions, broccoli and red bell peppers and saute for a couple of minutes until the onions are soft and tender. Add the curry paste and saute for a couple of more minutes. Stir in salt and add about 1-1/2 to 2 cups of water. You essentially want a thick gravy for the Broccoli Curry; hence add water accordingly. Give all the ingredients a good stir. Check the salt levels and adjust to suit your taste. Give the mixture a quick boil only when you are ready to serve. Serve the Broccoli & Vegetable Coconut Curry, along with Neer Dosa or Kerala (Lachha) Paratha or even plain steamed rice. | https://www.archanaskitchen.com/broccoli-and-vegetable-curry-recipe |
2399 | Andhra Style Makka Garelu Recipe (Corn Vada Recipe) | Andhra Style Makka Garelu Recipe (Corn Vada Recipe) | 1 cup Sweet corn,1/4 cup Gram flour (besan),1 Onion – chopped,2 Green Chillies,1 sprig Curry leaves,4 cloves Garlic,1 teaspoon Kalonji (Onion Nigella Seeds),Salt – to taste,Sunflower Oil | 1 cup Sweet corn,1/4 cup Gram flour (besan),1 Onion – chopped,2 Green Chillies,1 sprig Curry leaves,4 cloves Garlic,1 teaspoon Kalonji (Onion Nigella Seeds),Salt – to taste,Sunflower Oil | 10 | 30 | 40 | 4 | Andhra | Snack | Vegetarian | To begin making the Andhra Style Makka Garelu Recipe, blitz the corn in a mixer without water to a very chunky mixture.Transfer to a bowl, add the remaining ingredients including the besan and combine to make a thick garelu mixture. Check the salt and adjust to taste accordingly.Heat oil for deep frying. Once the oil is hot take small portions of the makka garelu mixture, flatten it to make a round disc and slide into the hot oil.Fry until the garelu is browned and crisp. Ensure you fry over medium heat, so it gets cooked evenly from inside out.Serve the Makka Garelu along with hot tea or dip it with a Date and Tamarind Chutney Recipe to enjoy your evening snack. | To begin making the Andhra Style Makka Garelu Recipe, blitz the corn in a mixer without water to a very chunky mixture.Transfer to a bowl, add the remaining ingredients including the besan and combine to make a thick garelu mixture. Check the salt and adjust to taste accordingly.Heat oil for deep frying. Once the oil is hot take small portions of the makka garelu mixture, flatten it to make a round disc and slide into the hot oil.Fry until the garelu is browned and crisp. Ensure you fry over medium heat, so it gets cooked evenly from inside out.Serve the Makka Garelu along with hot tea or dip it with a Date and Tamarind Chutney Recipe to enjoy your evening snack. | http://www.archanaskitchen.com/andhra-style-makka-garelu-recipe-maize-kernel-vada-recipe |
8717 | Vermicelli Kunafa Recipe With Paneer & Cream Filling | Vermicelli Kunafa Recipe With Paneer & Cream Filling | 100 grams Semiya (Vermicelli) – (thin variety),25 grams Butter – melted,1-1/2 cups Milk,50 grams Paneer (Homemade Cottage Cheese),1/4 tablespoon Corn flour,1/4 tablespoon Custard powder,1/2 tablespoon All Purpose Flour (Maida),1-1/2 tablespoons Condensed Milk,2 tablespoons Sugar,2 Cardamom (Elaichi) Pods/Seeds – ground,1/8 teaspoon Almond essence (Badam Essence),1/8 teaspoon Kewra essence,1/4 cup Mixed nuts – (I used Almond,1 cup Water,3 tablespoons Sugar,3 tablespoons Honey,1/2 teaspoon Rose water,1 tablespoon Lemon juice | 100 grams Semiya (Vermicelli) – (thin variety),25 grams Butter – melted,1-1/2 cups Milk,50 grams Paneer (Homemade Cottage Cheese),1/4 tablespoon Corn flour,1/4 tablespoon Custard powder,1/2 tablespoon All Purpose Flour (Maida),1-1/2 tablespoons Condensed Milk,2 tablespoons Sugar,2 Cardamom (Elaichi) Pods/Seeds – ground,1/8 teaspoon Almond essence (Badam Essence),1/8 teaspoon Kewra essence,1/4 cup Mixed nuts – (I used Almond,1 cup Water,3 tablespoons Sugar,3 tablespoons Honey,1/2 teaspoon Rose water,1 tablespoon Lemon juice | 30 | 45 | 75 | 6 | Fusion | Dessert | Vegetarian | For layering:To begin making Vermicelli Kunafa Recipe With Paneer & Cheese Cream Filling, start by preparing the layering. Break the vermicelli with fingers in a large bowl and add butter to it. Reserve some of it to roast the nuts later and greasing the baking tray.Now grease a baking tray with butter, always grease upward (I used silicon mold as it is easy to demold). Here you can use silicone cups as well where you can take out individual servings.Now place the vermicelli in the baking tray, press well with fingers, making around 1 inch high layer at the bottom.Preheat the oven on 200 degrees celsius.For Filling:Heat a deep sauce pan over low heat and pour milk into it.Add grated paneer to it.Now mix corn flour, custard powder & maida in some cold water. Mix well till so that there are no lumps in the mixture.Now pour the mixture in the saucepan with boiling milk and continue boiling with constant stirring with a wooden spatula.Now add sugar to it, and continue stirring. Add Almond extract & Kewra, stirring constantly till it gets a thick creamy textureNow add condensed milk, cardamom powder and switch off heat. Let it cool for some time.Assembling:For syrup, mix all the ingredients mentioned for syrup together in a saucepan & boil for 5-7 minutes or till thickened considerably, and switch off.Also in a small pan, heat butter and roast the chopped nuts till fragrant, on low heat such that the nuts aren’t burnt.Now pour the paneer mixture over the set vermicelli. Top the remaining vermicelli over the paneer mixture lightly, don’t press the vermicelli too much.Now bake for 45-50 minutes or till the the top layer of vermicelli becomes golden. Take care not to burn the vermicelli. Keep checking after 20 minutes in oven, since every oven has a different heating temperature.After it gets baked, take out the tray & let it rest for 5 mins then de-mold the cake cautiously, running a knife along the edges.Garnish with the roasted nuts. Reserve half of them for later.Now pour the prepared thickened honey syrup.Garnish with the remaining roasted nuts.Serve Vermicelli Kunafa Recipe With Paneer & Cheese Cream Filling as a festive snack when your relatives and guest visit you during this festive season. You can serve it along with Sattu Ka Namkeen Sharbat Recipe. | For layering:To begin making Vermicelli Kunafa Recipe With Paneer & Cheese Cream Filling, start by preparing the layering. Break the vermicelli with fingers in a large bowl and add butter to it. Reserve some of it to roast the nuts later and greasing the baking tray.Now grease a baking tray with butter, always grease upward (I used silicon mold as it is easy to demold). Here you can use silicone cups as well where you can take out individual servings.Now place the vermicelli in the baking tray, press well with fingers, making around 1 inch high layer at the bottom.Preheat the oven on 200 degrees celsius.For Filling:Heat a deep sauce pan over low heat and pour milk into it.Add grated paneer to it.Now mix corn flour, custard powder & maida in some cold water. Mix well till so that there are no lumps in the mixture.Now pour the mixture in the saucepan with boiling milk and continue boiling with constant stirring with a wooden spatula.Now add sugar to it, and continue stirring. Add Almond extract & Kewra, stirring constantly till it gets a thick creamy textureNow add condensed milk, cardamom powder and switch off heat. Let it cool for some time.Assembling:For syrup, mix all the ingredients mentioned for syrup together in a saucepan & boil for 5-7 minutes or till thickened considerably, and switch off.Also in a small pan, heat butter and roast the chopped nuts till fragrant, on low heat such that the nuts aren’t burnt.Now pour the paneer mixture over the set vermicelli. Top the remaining vermicelli over the paneer mixture lightly, don’t press the vermicelli too much.Now bake for 45-50 minutes or till the the top layer of vermicelli becomes golden. Take care not to burn the vermicelli. Keep checking after 20 minutes in oven, since every oven has a different heating temperature.After it gets baked, take out the tray & let it rest for 5 mins then de-mold the cake cautiously, running a knife along the edges.Garnish with the roasted nuts. Reserve half of them for later.Now pour the prepared thickened honey syrup.Garnish with the remaining roasted nuts.Serve Vermicelli Kunafa Recipe With Paneer & Cheese Cream Filling as a festive snack when your relatives and guest visit you during this festive season. You can serve it along with Sattu Ka Namkeen Sharbat Recipe. | http://www.archanaskitchen.com/vermicelli-kunafa-recipe-with-paneer-cream-filling |
7803 | Baked Cheesy Broccoli Pasta Recipe | Baked Cheesy Broccoli Pasta Recipe | 350 grams Penne pasta,2 cups Broccoli – florets,6 cloves Garlic – finely chopped,1/2 teaspoon Black pepper powder,2 teaspoons Italian seasoning,1 teaspoon Red Chilli flakes,1/4 cup Mozzarella cheese – grated,1 tablespoon Extra Virgin Olive Oil,Salt – to taste,2 tablespoons Whole Wheat Flour,1 cup Milk,1/4 cup Fresh cream,Salt and Pepper – to taste,1/4 teaspoon Nutmeg | 350 grams Penne pasta,2 cups Broccoli – florets,6 cloves Garlic – finely chopped,1/2 teaspoon Black pepper powder,2 teaspoons Italian seasoning,1 teaspoon Red Chilli flakes,1/4 cup Mozzarella cheese – grated,1 tablespoon Extra Virgin Olive Oil,Salt – to taste,2 tablespoons Whole Wheat Flour,1 cup Milk,1/4 cup Fresh cream,Salt and Pepper – to taste,1/4 teaspoon Nutmeg | 10 | 30 | 40 | 4 | Italian Recipes | Dinner | High Protein Vegetarian | To begin making the Broccoli Cheesy Pesto Pasta Recipe, first get all the ingredients ready.To Cook the Pasta Keep 500ml of water in a stock pot and allow it to boil, add in pasta along with a teaspoon of salt and allow it to boil and cook for at least 10-15 minutes until the pasta is cooked till al dente. Once the pasta is cooked,drain the water and rinse it under cold water to prevent it from getting overcooked in the heat. Drizzle some olive oil over the pasta and toss it well and keep the cooked pasta aside.To Make the Bechamel Sauce Whisk together the flours, salt, pepper, nutmeg and the milk until all the flour is well dissolved into the milk. Pour the white sauce mixture into a saucepan and begin cooking the Bechamel sauce until it thickens.As the White Sauce pasta mixture-Bechamel Sauce begins to thicken, add in the cream and continue to stir well until there are no lumps and the mixture is cooked through completely.Once the sauce is cooked and you are happy with the consistency, switch off the flame. To Make the Baked Cheesy Broccoli Pasta Preheat the oven to 180 degree celsius for 10 minutes and keep it ready. Heat a teaspoon of oil in a wok, add chopped garlic and saute till it softens. Add in the broccoli florets, teaspoon of salt, and roast them until cooked through. Sprinkle the italian seasonings, red chilli flakes and pepper powder and give it a stir. Add in your White sauce-Bechamel sauce along with pasta and mix well until the pasta is well coated.Check the salt and adjust to suit your taste.Turn off the heat and transfer the Broccoli Pasta to a ceramic baked pan, sprinkle with mozzarella cheese on top and bake in the oven till the cheese melts. Serve the Broccoli Cheesy Pesto Pasta Recipe along with a Watermelon Panzanella Salad and glass of wine for a perfect weeknight dinner with your partner or even pack into a lunchbox for kids. | To begin making the Broccoli Cheesy Pesto Pasta Recipe, first get all the ingredients ready.To Cook the Pasta Keep 500ml of water in a stock pot and allow it to boil, add in pasta along with a teaspoon of salt and allow it to boil and cook for at least 10-15 minutes until the pasta is cooked till al dente. Once the pasta is cooked,drain the water and rinse it under cold water to prevent it from getting overcooked in the heat. Drizzle some olive oil over the pasta and toss it well and keep the cooked pasta aside.To Make the Bechamel Sauce Whisk together the flours, salt, pepper, nutmeg and the milk until all the flour is well dissolved into the milk. Pour the white sauce mixture into a saucepan and begin cooking the Bechamel sauce until it thickens.As the White Sauce pasta mixture-Bechamel Sauce begins to thicken, add in the cream and continue to stir well until there are no lumps and the mixture is cooked through completely.Once the sauce is cooked and you are happy with the consistency, switch off the flame. To Make the Baked Cheesy Broccoli Pasta Preheat the oven to 180 degree celsius for 10 minutes and keep it ready. Heat a teaspoon of oil in a wok, add chopped garlic and saute till it softens. Add in the broccoli florets, teaspoon of salt, and roast them until cooked through. Sprinkle the italian seasonings, red chilli flakes and pepper powder and give it a stir. Add in your White sauce-Bechamel sauce along with pasta and mix well until the pasta is well coated.Check the salt and adjust to suit your taste.Turn off the heat and transfer the Broccoli Pasta to a ceramic baked pan, sprinkle with mozzarella cheese on top and bake in the oven till the cheese melts. Serve the Broccoli Cheesy Pesto Pasta Recipe along with a Watermelon Panzanella Salad and glass of wine for a perfect weeknight dinner with your partner or even pack into a lunchbox for kids. | http://www.archanaskitchen.com/broccoli-pesto-pasta-recipe |
155 | कटाची आमटी रेसिपी – Katachi Amti (Recipe In Hindi) | Katachi Amti (Recipe In Hindi) | 2 कप दाल का पानी – चना दाल का पानी,1 कप पानी – प्रयोग अनुसार,3 लॉन्ग,2 पूरी काली मिर्च,1 छोटा चमच्च जीरा,1 छोटा चमच्च राइ,1 तेज पत्ता,5 कढ़ी पत्ता – तोड़ ले,1 छोटा चमच्च गुड़ – कस ले,1 छोटा चमच्च इमली का पेस्ट – अपने अनुसार ज्यादा कम कर ले,1/2 छोटा चमच्च गोडा मसाला,1 बड़ा चमच्च तेल,हरा धनिया – थोड़ा,नमक – to taste | 2 cups lentil water – chana lentil water, 1 cup water – as per the experiment, 3 long, 2 whole black peppers, 1 teaspoon cumin, 1 teaspoon mustard, 1 bay leaf, 5 curry leaves – break, 1 teaspoon Jaggery – grated, 1 teaspoon tamarind paste – reduce as much as you like, 1/2 teaspoon goda masala, 1 tablespoon oil, coriander leaves – little, salt – to taste | 10 | 15 | 25 | 4 | Maharashtrian Recipes | Lunch | Vegetarian | कटाची आमटी बनाने के लिए सबसे पहले पानी को दाल के पानी में मिला ले. अलग से रख दे. अब एक सॉसपैन में तेल गरम करें। इसमें राइ डाले और 10 सेकण्ड्स तक पकने दे. 10 सेकण्ड्स के बाद ,इसमें लॉन्ग, तेज पत्ता और काली मिर्च डाले। 20 सेकण्ड्स तक पकाने के बाद इसमें जीरा, कढ़ी पत्ता और हरा धनिया डाले। 30 सेकण्ड्स बाद, दाल का पानी, गुड़, इमली का पेस्ट, गोडा मसाला और नमक डाले। उबाला आने तक पकाए। उबाला आने के बाद गैस बंद करें और परोसे। कटाची आमटी को पुरन पोली के साथ त्यौहार के दिनों में परोसे। | To make kachchi mango, first mix the water in the lentils. Keep it aside. Now heat the oil in a saucepan. Add mustard seeds and let it cook for 10 seconds. After 10 seconds, add long, bay leaves and black pepper. After cooking for 20 seconds, add cumin seeds, curry leaves and coriander leaves. After 30 seconds, add lentil water, jaggery, tamarind paste, goda masala and salt. Cook until it boils. Turn off the gas after boiling and serve. Serve Katachi Amati with Puran Poli on festival days. | https://www.archanaskitchen.com/katachi-amti-recipe-spicy-accompaniment-to-maharashtrian-puran-poli-in-hindi |
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Complete Interface
user_ingredients = ["onion", "tomato", "garlic", "ginger"] user_prep_time = 30 user_cook_time = 45 recommendations = recommend_recipes(user_ingredients, user_prep_time, user_cook_time) html_table = "<table><tr><th>Srno</th><th>Recipe Name</th><th>Translated Recipe Name</th><th>Ingredients</th><th>Translated Ingredients</th><th>Prep Time (mins)</th><th>Cook Time (mins)</th><th>Total Time (mins)</th><th>Servings</th><th>Cuisine</th><th>Course</th><th>Diet</th><th>Instructions</th><th>Translated Instructions</th><th>URL</th></tr>" for index in recommendations.index: recipe_info = df.loc[index] html_table += f"<tr><td>{recipe_info['Srno']}</td><td>{recipe_info['RecipeName']}</td><td>{recipe_info['TranslatedRecipeName']}</td><td>{recipe_info['Ingredients']}</td><td>{recipe_info['TranslatedIngredients']}</td><td>{recipe_info['PrepTimeInMins']}</td><td>{recipe_info['CookTimeInMins']}</td><td>{recipe_info['TotalTimeInMins']}</td><td>{recipe_info['Servings']}</td><td>{recipe_info['Cuisine']}</td><td>{recipe_info['Course']}</td><td>{recipe_info['Diet']}</td><td>{recipe_info['Instructions']}</td><td>{recipe_info['TranslatedInstructions']}</td><td>{recipe_info['URL']}</td></tr>" html_table += "</table>" HTML(html_table)
Conclusion
The Indian Food Recipes dataset provides a thorough and enlightening look into the varied realm of Indian cuisine.
Offering in-depth details on recipes, ingredients, cooking methods, and beyond, this dataset is a valuable tool for food lovers, researchers, and analysts.
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By utilizing this dataset, users can discover valuable information, spot new trends, and delve into the diverse array of flavors and customs that make up Indian gastronomy.
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