diff --git a/MachineLearning Projects/Food-Data-Analysis/README.md b/MachineLearning Projects/Food-Data-Analysis/README.md
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+# Food-Data-Analysis
+
+This is an EDA(Exploratory Data Analysis) project report of Indian food dishes.
+This project extensively utilizes classifciation along with data visualization using matplotlib to classify different Indian origin delicacies based on their taste, 
+state wise consumption, veg/non-veg and it's spicyness.
+
+Here are some of the results that we were able to derive based on our analysis.
+
+1) Coursewise distribution
+
+![image](https://user-images.githubusercontent.com/88388487/197797283-28112f8e-ffeb-4cec-b561-b4fd789b935f.png)
+
+
+
+2) Flavourwise distribution
+
+![image](https://user-images.githubusercontent.com/88388487/197797540-f9cb234d-5e6a-458a-bc59-bf1cd06c4b3a.png)
+
+
+
+3) Dietwise distribution
+
+![image](https://user-images.githubusercontent.com/88388487/197797758-1b5bdb8a-219f-4aa7-820b-ac38b3bf7f4c.png)
+
+
+
+4) Statewise distribution
+
+![image](https://user-images.githubusercontent.com/88388487/197797979-a3565d3a-6ab6-45fc-bea2-4347e69360bc.png)
+
+
+
+5) Region wise distribution
+
+![image](https://user-images.githubusercontent.com/88388487/197798161-a37c2ee2-aed6-4974-ab07-c8749f41d062.png)
+
diff --git a/MachineLearning Projects/Food-Data-Analysis/food_menu_data.csv b/MachineLearning Projects/Food-Data-Analysis/food_menu_data.csv
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+name,ingredients,diet,prep_time,cook_time,flavor_profile,course,state,region
+Balu shahi,"Maida flour, yogurt, oil, sugar",vegetarian,45,25,sweet,dessert,West Bengal,East
+Boondi,"Gram flour, ghee, sugar",vegetarian,80,30,sweet,dessert,Rajasthan,West
+Gajar ka halwa,"Carrots, milk, sugar, ghee, cashews, raisins",vegetarian,15,60,sweet,dessert,Punjab,North
+Ghevar,"Flour, ghee, kewra, milk, clarified butter, sugar, almonds, pistachio, saffron, green cardamom",vegetarian,15,30,sweet,dessert,Rajasthan,West
+Gulab jamun,"Milk powder, plain flour, baking powder, ghee, milk, sugar, water, rose water",vegetarian,15,40,sweet,dessert,West Bengal,East
+Imarti,"Sugar syrup, lentil flour",vegetarian,10,50,sweet,dessert,West Bengal,East
+Jalebi,"Maida, corn flour, baking soda, vinegar, curd, water, turmeric, saffron, cardamom",vegetarian,10,50,sweet,dessert,Uttar Pradesh,North
+Kaju katli,"Cashews, ghee, cardamom, sugar",vegetarian,10,20,sweet,dessert,-1,-1
+Kalakand,"Milk, cottage cheese, sugar",vegetarian,20,30,sweet,dessert,West Bengal,East
+Kheer,"Milk, rice, sugar, dried fruits",vegetarian,10,40,sweet,dessert,-1,-1
+Laddu,"Gram flour, ghee, sugar",vegetarian,10,40,sweet,dessert,-1,-1
+Lassi,"Yogurt, milk, nuts, sugar",vegetarian,5,5,sweet,dessert,Punjab,North
+Nankhatai,"Refined flour, besan, ghee, powdered sugar, yoghurt, green cardamom",vegetarian,20,30,sweet,dessert,-1,-1
+Petha,"Firm white pumpkin, sugar, kitchen lime, alum powder",vegetarian,10,30,sweet,dessert,Uttar Pradesh,North
+Phirni,"Rice, sugar, nuts",vegetarian,30,20,sweet,dessert,Odisha,East
+Rabri,"Condensed milk, sugar, spices, nuts",vegetarian,10,45,sweet,dessert,Uttar Pradesh,North
+Sheera,"Semolina, ghee, nuts, milk",vegetarian,10,25,sweet,dessert,Maharashtra,West
+Singori,"Khoa, coconut, molu leaf",vegetarian,10,20,sweet,dessert,Uttarakhand,North
+Sohan halwa,"Corn flour, ghee, dry fruits",vegetarian,10,60,sweet,dessert,Uttar Pradesh,North
+Sohan papdi,"Gram flour, ghee, sugar, milk, cardamom",vegetarian,-1,60,sweet,dessert,Maharashtra,West
+Chhena jalebi,"Chhena, sugar, ghee",vegetarian,10,50,sweet,dessert,Odisha,East
+Chhena kheeri,"Chhena, sugar, milk",vegetarian,-1,60,sweet,dessert,Odisha,East
+Chhena poda,"Sugar, chenna cheese",vegetarian,10,45,sweet,dessert,Odisha,East
+Cham cham,"Flour, cream, sugar, saffron, lemon juice, coconut flakes",vegetarian,40,60,sweet,dessert,West Bengal,East
+Kheer sagar,"Chenna, condensed milk, sugar, saffron, cardamom",vegetarian,25,60,sweet,dessert,Odisha,East
+Ledikeni,"Chhena, sugar, ghee",vegetarian,45,45,sweet,dessert,West Bengal,East
+Lyangcha,"Flour, fried milk power, sugar syrup",vegetarian,20,30,sweet,dessert,Assam,North East
+Malapua,"Yoghurt, refined flour, ghee, fennel seeds",vegetarian,10,120,sweet,dessert,Bihar,North
+Mihidana,"Besan flour, sugar, ghee",vegetarian,15,30,sweet,dessert,West Bengal,East
+Misti doi,"Milk, jaggery",vegetarian,480,30,sweet,dessert,West Bengal,East
+Pantua,"Chhena, sugar, ghee, flour",vegetarian,45,45,sweet,dessert,West Bengal,East
+Pithe,"Rice flour, wheat flour",vegetarian,15,35,sweet,dessert,Assam,North East
+Rasabali,"Chenna, sweetened milk",vegetarian,15,30,sweet,dessert,Odisha,East
+Ras malai,"Chhena, reduced milk, pistachio",vegetarian,180,60,sweet,dessert,West Bengal,East
+Rasgulla,"Chhena, sugar, cardamom",vegetarian,10,90,sweet,dessert,West Bengal,East
+Sandesh,"Milk, sugar, saffron, cardamom",vegetarian,30,20,sweet,dessert,West Bengal,East
+Adhirasam,"Rice flour, jaggery, ghee, vegetable oil, elachi",vegetarian,10,50,sweet,dessert,West Bengal,East
+Ariselu,"Rice flour, jaggery, ghee",vegetarian,15,75,sweet,dessert,Andhra Pradesh,South
+Bandar laddu,"Besan, jaggery, cardamom powder, ghee, cashews and raisins, jaggery syrup, sugar",vegetarian,5,35,sweet,dessert,Andhra Pradesh,South
+Chikki,"Peanuts, jaggery",vegetarian,10,20,sweet,dessert,Maharashtra,West
+Dharwad pedha,"Milk, Sugar, Dharwadi buffalo milk",vegetarian,20,60,sweet,dessert,Karnataka,South
+Double ka meetha,"Loaf bread, milk",vegetarian,20,30,sweet,dessert,Telangana,South
+Gavvalu,"Rice flour, sugar, salt, ghee, semolina",vegetarian,10,30,sweet,dessert,Andhra Pradesh,South
+Kakinada khaja,"Wheat flour, sugar",vegetarian,10,30,sweet,dessert,Andhra Pradesh,South
+Kuzhi paniyaram,"Black lentils, rice",vegetarian,10,20,sweet,dessert,Kerala,South
+Mysore pak,"Besan flour, semolina, mung bean, jaggery, coconut, skimmed milk powder, sugar, ghee",vegetarian,5,20,sweet,dessert,Karnataka,South
+Obbattu holige,"Maida flour, turmeric, coconut, chickpeas, jaggery, ghee, cardamom",vegetarian,180,60,sweet,main course,Karnataka,South
+Palathalikalu,"Rice flour, milk",vegetarian,10,25,sweet,dessert,Andhra Pradesh,South
+Poornalu,"Chana dal, jaggery",vegetarian,240,60,sweet,dessert,Andhra Pradesh,South
+Pongal,"Rice, jaggery, cashews, ghee",vegetarian,5,20,sweet,dessert,Tamil Nadu,South
+Pootharekulu,"Rice flour, powdered sugar, ghee",vegetarian,10,60,sweet,dessert,Andhra Pradesh,South
+Qubani ka meetha,"Apricots, sugar syrup",vegetarian,10,20,sweet,dessert,Telangana,South
+Sheer korma,"Vermicelli pudding, milk",vegetarian,10,20,sweet,dessert,Telangana,South
+Unni Appam,"Rice flour, banana, jaggery, coconut",vegetarian,10,20,sweet,dessert,Tamil Nadu,South
+Kajjikaya,"Rice flour, jaggery, coconut",vegetarian,40,15,sweet,dessert,Andhra Pradesh,South
+Anarsa,"Rice flour, jaggery, khus-khus seeds",vegetarian,10,50,sweet,dessert,Maharashtra,West
+Basundi,"Sugar, milk, nuts",vegetarian,10,35,sweet,dessert,Gujarat,West
+Dhondas,"Cucumber, rava",vegetarian,10,50,sweet,dessert,Maharashtra,West
+Doodhpak,"Milk, rice, sugar, dry fruits",vegetarian,10,60,sweet,dessert,Gujarat,West
+Mahim halwa,"Semolina, sugar",vegetarian,10,25,sweet,dessert,Maharashtra,West
+Modak,"Rice flour, coconut, jaggery ",vegetarian,10,15,sweet,dessert,Maharashtra,West
+Shankarpali,"Sugar, ghee, maida flour, semolina",vegetarian,10,35,sweet,dessert,Maharashtra,West
+Shrikhand,"Curd, sugar, saffron, cardamom",vegetarian,10,720,sweet,dessert,Maharashtra,West
+Sutar feni,"Maida, sugar, ghee",vegetarian,15,55,sweet,dessert,Maharashtra,West
+Maach Jhol,"Fish, potol, tomato, chillies, ginger, garlic",non vegetarian,10,40,spicy,main course,Assam,North East
+Pork Bharta,"Boiled pork, onions, chillies, ginger and garlic",non vegetarian,-1,-1,spicy,main course,Tripura,North East
+Chak Hao Kheer,"Rice, milk, sugar, cardamom",vegetarian,240,45,sweet,dessert,Manipur,North East
+Galho,"Rice, axone, salt, water, chillies, pork",non vegetarian,5,15,spicy,main course,Nagaland,North East
+Aloo gobi,"Cauliflower, potato, garam masala, turmeric, curry leaves",vegetarian,10,20,spicy,main course,Punjab,North
+Aloo tikki,"Rice flour, potato, bread crumbs, garam masala, salt",vegetarian,5,20,spicy,main course,Punjab,North
+Aloo matar,"Potato, peas, chillies, ginger, garam masala, garlic",vegetarian,5,40,spicy,main course,Punjab,North
+Aloo methi,"Potato, fenugreek leaves, chillies, salt, oil",vegetarian,10,40,bitter,main course,Punjab,North
+Aloo shimla mirch,"Potato, shimla mirch, garam masala, amchur powder, salt",vegetarian,10,40,spicy,main course,Punjab,North
+Bhatura,"Chole, rava, yogurt, plain flour, baking soda",vegetarian,20,20,spicy,main course,Punjab,North
+Bhindi masala,"Ladies finger, garam masala, kasuri methi, tomatoes, chili powder",vegetarian,20,30,spicy,main course,Punjab,North
+Biryani,"Chicken thighs, basmati rice, star anise, sweet, green chillies",non vegetarian,30,120,spicy,main course,Telangana,South
+Butter chicken,"Chicken, greek yogurt, cream, garam masala powder, cashew nuts, butter",non vegetarian,10,35,spicy,main course,NCT of Delhi,North
+Chana masala,"Chickpeas, tomato paste, garam masala, ginger, red onion, avocado oil",vegetarian,20,30,spicy,main course,Punjab,North
+Chapati,"Whole wheat flour, olive oil, hot water, all purpose flour",vegetarian,10,10,-1,main course,Maharashtra,West
+Chicken razala,"Chicken, dahi, sesame seeds, garam masala powder, cashew nuts, saffron",non vegetarian,10,35,spicy,main course,West Bengal,East
+Chicken Tikka masala,"Naan bread, tomato sauce, skinless chicken breasts, heavy cream, garam masala",non vegetarian,10,50,spicy,main course,Punjab,North
+Chicken Tikka,"Chicken, whole wheat bread, rice flour, garam masala powder, whole egg",non vegetarian,120,45,spicy,starter,Punjab,North
+Chole bhature,"Chole, bhatura, garam masala, bay leaf, cinnamon stick",vegetarian,20,30,spicy,main course,Punjab,North
+Daal baati churma,"Moong dal, masoor dal, chana dal, wheat flour, almond",vegetarian,10,90,spicy,main course,Rajasthan,West
+Daal puri,"Moong dal, garam masala powder, garlic, green chilli, all purpose flour",vegetarian,30,30,spicy,main course,West Bengal,East
+Dal makhani ,"Red kidney beans, urad dal, cream, garam masala, chili powder",vegetarian,10,60,sweet,main course,Punjab,North
+Dal tadka,"Pigeon peas, garam masala, ginger, red onion, kasuri methi",vegetarian,10,30,spicy,main course,Punjab,North
+Dum aloo,"Baby potatoes, garam masala, cashew nuts, kasuri methi, tomatoes",vegetarian,20,50,spicy,main course,Jammu & Kashmir,North
+Poha,"Beaten rice flakes, potato, curry leaves, green chilies, lemon juice",vegetarian,20,30,spicy,snack,Maharashtra,West
+Fara,"Chana dal, whole wheat flour, arhar dal, white urad dal, garam masala powder",vegetarian,10,60,spicy,snack,Chhattisgarh,Central
+Kachori,"Moong dal, rava, garam masala, dough, fennel seeds",vegetarian,30,60,spicy,snack,Uttar Pradesh,North
+Kadai paneer,"Cottage cheese, bell peppers, gravy, garam masala, cashew nuts",vegetarian,20,30,spicy,main course,Punjab,North
+Kadhi pakoda,"Besan, garam masala powder, gram flour, ginger, curry leaves",vegetarian,10,60,spicy,main course,Haryana,North
+Karela bharta,"Bitter gourd, fennel, garam masala powder, chili powder, amchur powder",vegetarian,20,50,bitter,main course,Punjab,North
+Khichdi,"Moong dal, green peas, ginger, tomato, green chili",vegetarian,40,20,spicy,main course,-1,-1
+Kofta,"Paneer, potato, cream, corn flour, garam masala",vegetarian,20,40,spicy,main course,Uttar Pradesh,North
+Kulfi falooda,"Rose syrup, falooda sev, mixed nuts, saffron, sugar",vegetarian,45,25,sweet,dessert,-1,-1
+Lauki ke kofte,"Bottle gourd, garam masala powder, gram flour, ginger, chillies",vegetarian,20,40,spicy,main course,Uttar Pradesh,North
+Lauki ki subji,"Bottle gourd, coconut oil, garam masala, ginger, green chillies",vegetarian,10,20,spicy,main course,-1,-1
+Litti chokha,"Wheat flour, roasted gram flour, tomato, nigella seeds, chilli",vegetarian,45,60,spicy,main course,Bihar,North
+Makki di roti sarson da saag,"Palak, makki atta, mustard green, garam masala, ginger",vegetarian,25,30,spicy,main course,Punjab,North
+Misi roti,"Whole wheat flour, chickpea flour, green chilies",vegetarian,30,30,spicy,main course,Punjab,North
+Mushroom do pyaza,"Mushroom, malai, garam masala, ginger, capsicum",vegetarian,10,30,spicy,main course,Punjab,North
+Mushroom matar,"Canned coconut milk, frozen green peas, wild mushrooms, garam masala, tomatoes",vegetarian,10,30,spicy,main course,Punjab,North
+Naan,"Whole wheat flour, honey, butter, garlic",vegetarian,60,30,-1,main course,Punjab,North
+Navrattan korma,"Green beans, potatoes, khus khus, low fat, garam masala powder",vegetarian,25,40,spicy,main course,Uttar Pradesh,North
+Palak paneer,"Cottage cheese, palak, cream, garam masala, butter",vegetarian,20,40,spicy,main course,Punjab,North
+Paneer butter masala,"Paneer, whipping cream, garam masala, cashew nuts, butter",vegetarian,20,40,spicy,main course,Punjab,North
+Paneer tikka masala,"Paneer, greek yogurt, tandoori masala, cream, bell pepper",vegetarian,20,40,spicy,main course,Punjab,North
+Pani puri,"Kala chana, mashed potato, boondi, sev, lemon",vegetarian,15,2,spicy,snack,-1,-1
+Panjeeri,"Whole wheat flour, musk melon seeds, poppy seeds, edible gum, semolina",vegetarian,10,25,sweet,dessert,Uttar Pradesh,
+Papad,"Urad dal, sev, lemon juice, chopped tomatoes",vegetarian,5,5,spicy,snack,-1,-1
+Paratha,"Wheat flour, butter, potato, coriander",vegetarian,15,45,spicy,main course,Punjab,North
+Pattor,"Arbi ke patte, sesame seeds, gur, bengal gram flour, imli",vegetarian,10,30,spicy,main course,Rajasthan,West
+Pindi chana,"Fennel, tea bags, tomato, kasuri methi, cinnamon",vegetarian,500,120,spicy,main course,Punjab,North
+Rajma chaval,"Red kidney beans, garam masala powder, ginger, tomato, mustard oil",vegetarian,15,90,spicy,main course,-1,North
+Rongi,"Garam masala powder, tomato, kasuri methi, cinnamon, mustard oil",vegetarian,10,30,-1,main course,Punjab,North
+Samosa,"Potatoes, green peas, garam masala, ginger, dough",vegetarian,30,30,spicy,snack,-1,-1
+Sattu ki roti,"Sattu, atta, dough, filling, mustard oil",vegetarian,10,20,spicy,main course,Bihar,North
+Shahi paneer,"Cottage cheese, malai, garam masala, ginger, tomato",vegetarian,20,40,spicy,main course,Punjab,North
+Shahi tukra,"Rose water, milk, white bread slices, saffron, almonds",vegetarian,10,30,sweet,dessert,Telangana,South
+Vegetable jalfrezi,"Baby corn, french beans, garam masala, ginger, carrot",vegetarian,10,30,spicy,main course,Punjab,North
+Tandoori Chicken,"Greek yogurt, garam masala, kasuri methi, marinade, mustard oil",non vegetarian,240,30,spicy,main course,Punjab,North
+Tandoori Fish Tikka,"Chickpea flour, biryani masala powder, yogurt, fish fillets, green bell pepper",non vegetarian,240,30,spicy,starter,Punjab,North
+Attu,"Whole wheat flour, arhar dal, ginger, kala jeera, green chilli",vegetarian,150,25,spicy,snack,Andhra Pradesh,South
+Avial,"Raw banana, elephant foot yam, long beans, tindora, urad dal",vegetarian,30,45,spicy,main course,Kerala,South
+Bisi bele bath,"Split pigeon peas, chana dal, urad dal, green peas, french beans",vegetarian,30,45,spicy,main course,Karnataka,South
+Currivepillai sadam ,"Chana dal, urad dal, fresh coconut, sesame seeds, curry leaves",vegetarian,10,20,spicy,main course,Tamil Nadu,South
+Dosa,"Chana dal, urad dal, whole urad dal, blend rice, rock salt",vegetarian,360,90,spicy,snack,-1,South
+Idiappam,"Rice flour, hot water, grated coconut",vegetarian,120,30,spicy,snack,Tamil Nadu,South
+Idli,"Split urad dal, urad dal, idli rice, thick poha, rock salt",vegetarian,360,90,spicy,snack,-1,South
+Kanji,"Carrot, yellow mustard, red chilli, black salt",vegetarian,10,45,-1,snack,Kerala,South
+Kaara kozhambu,"Sesame oil, drumstick, tamarind paste, sambar powder, tomato",vegetarian,-1,-1,spicy,main course,Tamil Nadu,South
+Keerai kootu,"Moong dal, chana dal, spinach, urad dal, coconut oil",vegetarian,20,30,spicy,main course,Tamil Nadu,South
+Keerai masiyal,"Urad dal, curry leaves, sugar, mustard seeds, spinach",vegetarian,-1,-1,spicy,main course,Tamil Nadu,South
+Keerai sadam,"Greens, tomato, mustard seeds, fenugreek seeds",vegetarian,10,20,spicy,main course,Tamil Nadu,South
+Keerai poriyal,"Amaranth leaves, split urad dal, mustard seeds, grated coconut, red chili",vegetarian,20,30,spicy,main course,Tamil Nadu,South
+Beef Fry,"Beef, coconut, garam masala, curry leaves, green chilies, chili powder",non vegetarian,10,60,spicy,main course,Kerala,South
+Kootu,"Chana dal, urad dal, potato, beans, peas",vegetarian,10,15,spicy,main course,Tamil Nadu,South
+Kos kootu,"Moong dal, chana dal, cabbage, tamarind, curry leaves",vegetarian,10,20,spicy,main course,Tamil Nadu,South
+Koshambri,"Moong dal, cucumber, curry leaves, green chili, lemon juice",vegetarian,10,20,spicy,main course,Karnataka,South
+Kothamali sadam,"Chana dal, urad dal, gooseberry, raw rice, curry leaves",vegetarian,10,20,spicy,main course,Tamil Nadu,South
+Kuzhakkattai,"Sesame oil, raw rice, jaggery, grated coconut",vegetarian,10,90,spicy,main course,Tamil Nadu,South
+Kuzhambu,"Pearl onions, urad dal, drumsticks, tomato, curry leaves",vegetarian,5,30,spicy,main course,Tamil Nadu,South
+Masala Dosa,"Chana dal, urad dal, potatoes, idli rice, thick poha",vegetarian,360,90,spicy,snack,-1,South
+Pachadi,"Coconut oil, cucumber, curd, curry leaves, mustard seeds",vegetarian,10,25,-1,main course,-1,South
+Paniyaram,"Yogurt, ginger, curry leaves, baking soda, green chilli",vegetarian,10,20,-1,main course,Tamil Nadu,South
+Papadum,"Lentils, black pepper, vegetable oil",vegetarian,5,5,spicy,snack,Kerala,South
+Paravannam,"Raw rice,  jaggery, milk",vegetarian,-1,-1,spicy,main course,Kerala,South
+Payasam,"Rice, cashew nuts, milk, raisins, sugar",vegetarian,15,30,sweet,dessert,-1,South
+Paruppu sadam,"Arhar dal, sambar powder, tomato, curry leaves, fennel seeds",vegetarian,10,20,-1,main course,Tamil Nadu,South
+Pesarattu,"Green moong beans, rice flour",vegetarian,120,20,spicy,snack,Andhra Pradesh,South
+Poriyal,"Chana dal, urad dal, beans, coconut, mustard",vegetarian,-1,-1,spicy,main course,Tamil Nadu,South
+Puli sadam,"Urad dal, lemon, tamarind, cooked rice, curry leaves",vegetarian,10,20,-1,main course,Tamil Nadu,South
+Rasam,"Tomato, curry leaves, garlic, mustard seeds, hot water",vegetarian,10,35,spicy,main course,-1,South
+Puttu,"Brown rice flour, sugar, grated coconut",vegetarian,495,40,-1,main course,Kerala,South
+Sambar,"Pigeon peas, eggplant, drumsticks, sambar powder, tamarind",vegetarian,20,45,spicy,main course,-1,South
+Sandige,"Thin rice flakes, black sesame seeds, curry leaves",vegetarian,120,60,-1,main course,Karnataka,South
+Sevai,"Sevai, parboiled rice, steamer",vegetarian,120,30,-1,main course,-1,South
+Thayir sadam,"Urad dal, curd, sesame oil, ginger, curry leaves, mustard seeds",vegetarian,10,20,-1,main course,Tamil Nadu,South
+Theeyal,"Coconut, whole red beans, masala, sesame oil, tamarind",vegetarian,15,20,-1,main course,Kerala,South
+Uttapam,"Chana dal, urad dal, thick poha, tomato, butter",vegetarian,10,20,spicy,snack,-1,South
+Vada,"Urad dal, ginger, curry leaves, green chilies, black pepper",vegetarian,15,20,spicy,snack,-1,South
+Chicken Varuval,"Meat curry powder, chicken chunks, ginger, tomato, cinnamon",non vegetarian,10,35,spicy,main course,Tamil Nadu,South
+Upma,"Chana dal, urad dal, ginger, curry leaves, sugar",vegetarian,10,20,spicy,snack,-1,-1
+Amti,"Kala masala, arhar dal, curry leaves, mustard seeds, hot water",vegetarian,10,45,spicy,main course,Maharashtra,West
+Zunka,"Gram flour, mustard, garlic, turmeric, red chilli",vegetarian,10,25,spicy,main course,Maharashtra,West
+Kolim Jawla,"Baingan, fish, coconut oil, fresh coconut, ginger",non vegetarian,-1,-1,spicy,main course,Maharashtra,West
+Saath,"Urad dal, potatoes, wheat flour, sooji",vegetarian,20,40,spicy,main course,Gujarat,West
+Bajri no rotlo,"Wheat flour, pearl millet flour, hot water",vegetarian,10,10,spicy,main course,Gujarat,West
+Coconut vadi,"Condensed milk, mawa, desiccated coconut, almonds, cashews",vegetarian,20,30,sweet,dessert,Maharashtra,West
+Bhakri,"Jowar flour, sesame seeds",vegetarian,20,25,-1,main course,Maharashtra,West
+Bombil fry,"Bombay duck, malvani masala, rice flour, bombay rava, green chilies",non vegetarian,-1,-1,spicy,main course,Maharashtra,West
+Chakali,"Rice flour, sesame, plain flour, turmeric, red chilli",vegetarian,10,40,spicy,snack,Maharashtra,West
+Chevdo,"Citric acid, fry, raisins, sugar, chana daal",vegetarian,20,30,spicy,snack,Gujarat,West
+Chorafali,"Urad dal, bengal gram flour, dried mango, baking soda, black salt",vegetarian,5,15,spicy,snack,Gujarat,West
+Copra paak,"Condensed milk, nestle cream, coconut ice, red food coloring, desiccated coconut",vegetarian,20,30,-1,main course,Gujarat,West
+Daal Dhokli,"Whole wheat flour, dal, kokum, gur, bengal gram flour",vegetarian,20,30,spicy,main course,Gujarat,West
+Kutchi dabeli,"Pav, aloo, peanut, pomegranate, star anise",vegetarian,30,10,spicy,snack,Gujarat,West
+Dahi vada,"Urad dal, bhuna chana, garam masala, dates, tamarind",vegetarian,30,30,-1,snack,Maharashtra,West
+Dalithoy,"Arhar dal, coconut oil, curry leaves, mustard seeds, red chilli",vegetarian,5,20,-1,main course,Maharashtra,West
+Dhokla,"Rava, coconut, gram flour, mustard, sesame",vegetarian,10,20,spicy,snack,Gujarat,West
+Dudhi halwa,"Bottle gourd, green, raisins, sugar, clarified butter",vegetarian,15,30,sweet,dessert,Gujarat,West
+Gatta curry,"Yogurt, besan, sauce, garam masala powder, gram flour",vegetarian,10,20,spicy,main course,Rajasthan,West
+Gud papdi,"Wheat flour, jaggery, clarified butter, sliced almonds",vegetarian,10,20,sweet,dessert,Gujarat,West
+Ghooghra,"Dry fruits, semolina, all purpose flour",vegetarian,-1,-1,spicy,snack,Gujarat,West
+Handwo,"Bottle gourd, chana dal, cabbage, urad dal, toor dal",vegetarian,20,30,spicy,snack,Gujarat,West
+Halvasan,"Whole wheat rava, chia seed, lemon, edible gum, litre milk",vegetarian,-1,-1,sweet,dessert,Gujarat,West
+Jeera Aloo,"Green chilies, lemon juice, chili powder, boiled potatoes",vegetarian,10,30,spicy,main course,Gujarat,West
+Kansar,"Wheat flour, cashews, rapeseed oil",vegetarian,10,40,-1,main course,Gujarat,West
+Keri no ras,"Mango, sugar",vegetarian,10,10,sour,main course,Gujarat,West
+Khakhra,"Whole wheat flour, low fat, bengal gram flour",vegetarian,20,40,spicy,snack,Gujarat,West
+Khandvi,"Green chili paste, white sesame seeds, gram flour, curry leaves, green chili",vegetarian,20,45,spicy,snack,Gujarat,West
+Kombdi vade,"Rice flour, urad dal, wheat flour, gram flour, turmeric",vegetarian,10,25,spicy,snack,Maharashtra,West
+Laapsi,"Cinnamon, jaggery, clarified butter, dry roasted",vegetarian,10,40,spicy,main course,Madhya Pradesh,Central
+Koshimbir,"Cucumber, carrot, tomatoes, cilantro",vegetarian,10,10,spicy,main course,Maharashtra,West
+Methi na Gota,"Rava, gram flour, lemon juice, turmeric, fenugreek leaves",vegetarian,15,30,bitter,main course,Gujarat,West
+Mohanthal,"Rose water, pistachio, badam, bengal gram flour, saffron",vegetarian,5,45,sweet,dessert,Gujarat,West
+Muthiya,"Bottle gourd, whole wheat flour, rava, sesame seeds, bengal gram flour",vegetarian,15,30,bitter,snack,Gujarat,West
+Patra,"Arbi ke patte, sesame seeds, gur, bengal gram flour, imli",vegetarian,10,40,spicy,snack,Gujarat,West
+Pav Bhaji,"Pav bhaji masala, gobi, potatoes, green peas, dinner rolls",vegetarian,20,40,spicy,main course,Maharashtra,West
+Puri Bhaji,"Aloo, urad dal, mustard, ginger, curry leaves",vegetarian,10,30,spicy,main course,Maharashtra,West
+Sabudana Khichadi,"Raw peanuts, sabudana, lemon, avocado oil, curry leaves, green chili",vegetarian,70,30,spicy,snack,Maharashtra,West
+Sev khamani,"Khaman, pomegranate, sev, powdered sugar, garlic",vegetarian,20,30,spicy,main course,Gujarat,West
+Sev tameta,"Sev, ginger, tomato, sugar",vegetarian,20,30,spicy,main course,Gujarat,West
+Namakpara,"Wheat flour, baking soda, all purpose flour, black pepper, sunflower oil",vegetarian,10,35,spicy,snack,Gujarat,West
+Sukhdi,"Whole wheat flour, gur, clarified butter",vegetarian,10,20,sweet,dessert,Maharashtra,West
+Surnoli,"Rice flakes, yogurt, raw rice, jaggery, grated coconut",vegetarian,60,10,spicy,snack,Maharashtra,West
+Thalipeeth,"Whole wheat flour, rice flour, pearl millet flour, sorghum flour, sesame seeds",vegetarian,25,30,spicy,main course,Maharashtra,West
+Undhiyu,"Sweet potato, surti papdi, baby potatoes, valor papdi, green peas",vegetarian,25,60,spicy,main course,Gujarat,West
+Veg Kolhapuri,"Gobi, potato, beans, khus khus, coconut",vegetarian,20,30,spicy,main course,Maharashtra,West
+Vindaloo,"Chicken, coconut oil, wine vinegar, ginger, green, cinnamon",non vegetarian,10,40,spicy,main course,Goa,West
+Lilva Kachori,"Green garlic chutney, fresh green peas, ginger, lemon juice, plain flour",vegetarian,30,6,spicy,snack,Gujarat,West
+Mag Dhokli,"Moong beans, jaggery, red chillies, oil, salt",vegetarian,-1,-1,spicy,snack,Gujarat,West
+Khichu,"Rice flour, sesame seeds, baking soda, peanut oil",vegetarian,5,10,spicy,snack,Gujarat,West
+Thepla,"Chickpea flour, methi leaves, jowar flour, wheat flour",vegetarian,15,30,spicy,snack,Gujarat,West
+Farsi Puri,"Semolina, clarified butter, oil, white flour, black pepper",vegetarian,-1,-1,-1,snack,Gujarat,West
+Khaman,"Yogurt, fresh coconut, sesame seeds, semolina, gram flour",vegetarian,10,20,spicy,snack,Gujarat,West
+Turiya Patra Vatana sabji,"Ridge gourd, baking soda, sugar, grated coconut, peas",vegetarian,35,40,spicy,main course,Gujarat,West
+Churma Ladoo,"Whole wheat flour, khus khus, sesame seeds, dry coconut, gur",vegetarian,12,40,sweet,dessert,Rajasthan,West
+Cheera Doi,"Rice, mango, curd",vegetarian,-1,-1,sweet,dessert,West Bengal,East
+Gheela Pitha,"Sticky rice, rice flour, jaggery, orange rind",vegetarian,10,30,sweet,dessert,Assam,North East
+Khar,"Raw papaya, panch phoran masala, nigella seeds, mustard oil, fennel seeds",vegetarian,10,20,-1,main course,Assam,North East
+Kumol Sawul,"Rice, eggs, carrot, beetroot",non vegetarian,-1,-1,spicy,main course,Assam,North East
+Luchi,"Maida, vegetable oil",vegetarian,20,30,-1,main course,West Bengal,East
+Alu Pitika,"Potatoes, mustard oil, fish, green chillies",non vegetarian,5,20,spicy,main course,Assam,North East
+Masor tenga,"Ridge gourd, fish, lemon, tomatoes, mustard oil",non vegetarian,15,25,spicy,main course,Assam,North East
+Bengena Pitika,"Brinjal, onions, salt, sesame seeds, coriander",vegetarian,-1,-1,-1,main course,Assam,North East
+Bilahi Maas,"Potatoes, garam masala, tomatoes, mustard oil, bay leaf",non vegetarian,10,20,-1,main course,Assam,North East
+Black rice,"Forbidden black rice, chicken, olive oil, slivered almonds, garlic powder",non vegetarian,-1,-1,-1,main course,Manipur,North East
+Bora Sawul,"Biryani masala, mixed vegetables, yellow moong daal, whole red, mustard seeds",vegetarian,-1,-1,spicy,main course,Assam,North East
+Brown Rice,"Brown rice, soy sauce, olive oil",vegetarian,15,25,-1,main course,-1,-1
+Chingri malai curry,"Coconut milk, lobster, fresh green chilli, ginger, red onion",non vegetarian,10,40,spicy,main course,West Bengal,East
+Goja,"Baking soda, clarified butter, oil, all purpose flour",vegetarian,20,60,sweet,dessert,West Bengal,East
+Hando Guri,"Jaggery, raisins",vegetarian,-1,-1,sweet,dessert,Assam,North East
+Haq Maas,"Lamb, garam masala powder, curd, turmeric, bay leaf",non vegetarian,20,40,spicy,main course,Assam,North East
+Chingri Bhape,"Coconut, prawns, curd, mustard seed, green chili",non vegetarian,15,30,-1,main course,West Bengal,East
+Kabiraji,"Fish fillet, besan, lemon, mint, ginger",non vegetarian,-1,-1,spicy,main course,West Bengal,East
+Khorisa,"Fermented bamboo shoot, potato, ginger, green, mustard oil",vegetarian,-1,-1,spicy,main course,Assam,North East
+Koldil Chicken,"Banana flower, chicken, green chili, mustard oil, lemon juice",non vegetarian,-1,-1,spicy,main course,Assam,North East
+Konir Dom,"Aloo, tomatoes, mustard oil, bay leaf, cinnamon stick",non vegetarian,-1,-1,spicy,main course,Assam,North East
+Koldil Duck,"Rice flour, mutton, banana, gram flour, olive oil, baking powder",non vegetarian,-1,-1,spicy,main course,Assam,North East
+Masor Koni,"Fish roe, pumpkin flowers, mustard oil, turmeric, tomato",non vegetarian,-1,-1,spicy,main course,Assam,North East
+Mishti Chholar Dal,"Chana dal, fresh coconut, ginger, cinnamon, raisins",vegetarian,10,30,sweet,main course,West Bengal,East
+Pakhala,"Curd, cooked rice, curry leaves, dry chilli",vegetarian,-1,-1,-1,main course,Odisha,East
+Pani Pitha,"Tea leaves, white sesame seeds, dry coconut, soaked rice",vegetarian,10,20,-1,main course,Assam,North East
+Payokh,"Basmati rice, rose water, sugar, clarified butter, cardamom pods",vegetarian,-1,-1,sweet,dessert,Assam,North East
+Prawn malai curry,"Coconut milk, prawns, garlic, turmeric, sugar",non vegetarian,15,50,spicy,main course,West Bengal,East
+Red Rice,"Red pepper, red onion, butter, watercress, olive oil",vegetarian,-1,-1,-1,main course,-1,-1
+Shukto,"Green beans, bitter gourd, ridge gourd, banana, brinjal",vegetarian,10,20,spicy,main course,West Bengal,East
+Til Pitha,"Glutinous rice, black sesame seeds, gur",vegetarian,5,30,sweet,dessert,Assam,North East
+Bebinca,"Coconut milk, egg yolks, clarified butter, all purpose flour",vegetarian,20,60,sweet,dessert,Goa,West
+Shufta,"Cottage cheese, dry dates, dried rose petals, pistachio, badam",vegetarian,-1,-1,sweet,dessert,Jammu & Kashmir,North
+Mawa Bati,"Milk powder, dry fruits, arrowroot powder, all purpose flour",vegetarian,20,45,sweet,dessert,Madhya Pradesh,Central
+Pinaca,"Brown rice, fennel seeds, grated coconut, black pepper, ginger powder",vegetarian,-1,-1,sweet,dessert,Goa,West
\ No newline at end of file
diff --git a/MachineLearning Projects/Food-Data-Analysis/foodanalysis.ipynb b/MachineLearning Projects/Food-Data-Analysis/foodanalysis.ipynb
new file mode 100644
index 00000000..0616d5d7
--- /dev/null
+++ b/MachineLearning Projects/Food-Data-Analysis/foodanalysis.ipynb	
@@ -0,0 +1,1686 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "MG7tZCRtMnoO"
+      },
+      "source": [
+        "**FOOD DATA ANALYSIS**"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "LvJvhj5LZ1ua",
+        "outputId": "555e1cd4-3d5d-48a0-b5a6-23ffbe186ff5"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "\u001b[?25l\r\u001b[K     |████▊                           | 10 kB 26.6 MB/s eta 0:00:01\r\u001b[K     |█████████▌                      | 20 kB 7.7 MB/s eta 0:00:01\r\u001b[K     |██████████████▎                 | 30 kB 10.9 MB/s eta 0:00:01\r\u001b[K     |███████████████████             | 40 kB 4.7 MB/s eta 0:00:01\r\u001b[K     |███████████████████████▉        | 51 kB 4.4 MB/s eta 0:00:01\r\u001b[K     |████████████████████████████▋   | 61 kB 5.2 MB/s eta 0:00:01\r\u001b[K     |████████████████████████████████| 68 kB 3.1 MB/s \n",
+            "\u001b[?25h  Building wheel for uuid (setup.py) ... \u001b[?25l\u001b[?25hdone\n"
+          ]
+        }
+      ],
+      "source": [
+        "!pip install jovian --upgrade -q\n",
+        "import jovian\n",
+        "jovian.utils.colab.set_colab_file_id('1hO4YEBFiixx9_y5MwfoCvCd5QE6Zu9b6')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "ruGy_PRVcspK"
+      },
+      "source": [
+        "**Downloading the Dataset**"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "Fi9viouCZ4AA"
+      },
+      "outputs": [],
+      "source": [
+        "!pip install jovian opendatasets --upgrade --quiet"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "0jT4tZC1Z7eo"
+      },
+      "outputs": [],
+      "source": [
+        "# Defining Dataset URL\n",
+        "dataset_url = 'https://www.kaggle.com/nehaprabhavalkar/indian-food-101' "
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "1sqCDU72QByF"
+      },
+      "source": [
+        "**STEPS TO GET THE KAGGLE USERNAME AND KEY**\n",
+        "\n",
+        "i. Sign in to your kaggle account\n",
+        "\n",
+        "ii. Click on your profile at the top-right corner of the screen\n",
+        "\n",
+        "iii. Go to account\n",
+        "\n",
+        "iv. Scroll down to API\n",
+        "\n",
+        "v. Click on the \"Create New API Token\""
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "CZibmrgbZ_iQ",
+        "outputId": "f78fef35-b299-493a-b918-175e85d868f8"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Please provide your Kaggle credentials to download this dataset. Learn more: http://bit.ly/kaggle-creds\n",
+            "Your Kaggle username: nadarjegan\n",
+            "Your Kaggle Key: ··········\n",
+            "Downloading indian-food-101.zip to ./indian-food-101\n"
+          ]
+        },
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "100%|██████████| 6.79k/6.79k [00:00<00:00, 7.33MB/s]"
+          ]
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "\n"
+          ]
+        },
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "\n"
+          ]
+        }
+      ],
+      "source": [
+        "import opendatasets as od\n",
+        "od.download(dataset_url)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "_cAXe5muaBDc"
+      },
+      "outputs": [],
+      "source": [
+        "!pip install jovian --upgrade -q"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Npcfs8SVccX7"
+      },
+      "source": [
+        "**Data Preparation and Cleaning**\n",
+        "\n",
+        "Importing pandas library\n",
+        "\n",
+        "Creating pandas data frames\n",
+        "\n",
+        "Analysing the No. of rows & columns\n",
+        "\n",
+        "Droping the rows with Null values and invalid entries."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "syNHqV0qaLKj"
+      },
+      "outputs": [],
+      "source": [
+        "import pandas as pd"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 424
+        },
+        "id": "8o1Rg-c7aM9z",
+        "outputId": "b416a197-536b-414f-aebb-e27f7b913238"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/html": [
+              "\n",
+              "  <div id=\"df-113c5967-9484-46a8-974b-637c7b7a6f1a\">\n",
+              "    <div class=\"colab-df-container\">\n",
+              "      <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>name</th>\n",
+              "      <th>ingredients</th>\n",
+              "      <th>diet</th>\n",
+              "      <th>prep_time</th>\n",
+              "      <th>cook_time</th>\n",
+              "      <th>flavor_profile</th>\n",
+              "      <th>course</th>\n",
+              "      <th>state</th>\n",
+              "      <th>region</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>Balu shahi</td>\n",
+              "      <td>Maida flour, yogurt, oil, sugar</td>\n",
+              "      <td>vegetarian</td>\n",
+              "      <td>45</td>\n",
+              "      <td>25</td>\n",
+              "      <td>sweet</td>\n",
+              "      <td>dessert</td>\n",
+              "      <td>West Bengal</td>\n",
+              "      <td>East</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>Boondi</td>\n",
+              "      <td>Gram flour, ghee, sugar</td>\n",
+              "      <td>vegetarian</td>\n",
+              "      <td>80</td>\n",
+              "      <td>30</td>\n",
+              "      <td>sweet</td>\n",
+              "      <td>dessert</td>\n",
+              "      <td>Rajasthan</td>\n",
+              "      <td>West</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>Gajar ka halwa</td>\n",
+              "      <td>Carrots, milk, sugar, ghee, cashews, raisins</td>\n",
+              "      <td>vegetarian</td>\n",
+              "      <td>15</td>\n",
+              "      <td>60</td>\n",
+              "      <td>sweet</td>\n",
+              "      <td>dessert</td>\n",
+              "      <td>Punjab</td>\n",
+              "      <td>North</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>Ghevar</td>\n",
+              "      <td>Flour, ghee, kewra, milk, clarified butter, su...</td>\n",
+              "      <td>vegetarian</td>\n",
+              "      <td>15</td>\n",
+              "      <td>30</td>\n",
+              "      <td>sweet</td>\n",
+              "      <td>dessert</td>\n",
+              "      <td>Rajasthan</td>\n",
+              "      <td>West</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>Gulab jamun</td>\n",
+              "      <td>Milk powder, plain flour, baking powder, ghee,...</td>\n",
+              "      <td>vegetarian</td>\n",
+              "      <td>15</td>\n",
+              "      <td>40</td>\n",
+              "      <td>sweet</td>\n",
+              "      <td>dessert</td>\n",
+              "      <td>West Bengal</td>\n",
+              "      <td>East</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>...</th>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>250</th>\n",
+              "      <td>Til Pitha</td>\n",
+              "      <td>Glutinous rice, black sesame seeds, gur</td>\n",
+              "      <td>vegetarian</td>\n",
+              "      <td>5</td>\n",
+              "      <td>30</td>\n",
+              "      <td>sweet</td>\n",
+              "      <td>dessert</td>\n",
+              "      <td>Assam</td>\n",
+              "      <td>North East</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>251</th>\n",
+              "      <td>Bebinca</td>\n",
+              "      <td>Coconut milk, egg yolks, clarified butter, all...</td>\n",
+              "      <td>vegetarian</td>\n",
+              "      <td>20</td>\n",
+              "      <td>60</td>\n",
+              "      <td>sweet</td>\n",
+              "      <td>dessert</td>\n",
+              "      <td>Goa</td>\n",
+              "      <td>West</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>252</th>\n",
+              "      <td>Shufta</td>\n",
+              "      <td>Cottage cheese, dry dates, dried rose petals, ...</td>\n",
+              "      <td>vegetarian</td>\n",
+              "      <td>-1</td>\n",
+              "      <td>-1</td>\n",
+              "      <td>sweet</td>\n",
+              "      <td>dessert</td>\n",
+              "      <td>Jammu &amp; Kashmir</td>\n",
+              "      <td>North</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>253</th>\n",
+              "      <td>Mawa Bati</td>\n",
+              "      <td>Milk powder, dry fruits, arrowroot powder, all...</td>\n",
+              "      <td>vegetarian</td>\n",
+              "      <td>20</td>\n",
+              "      <td>45</td>\n",
+              "      <td>sweet</td>\n",
+              "      <td>dessert</td>\n",
+              "      <td>Madhya Pradesh</td>\n",
+              "      <td>Central</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>254</th>\n",
+              "      <td>Pinaca</td>\n",
+              "      <td>Brown rice, fennel seeds, grated coconut, blac...</td>\n",
+              "      <td>vegetarian</td>\n",
+              "      <td>-1</td>\n",
+              "      <td>-1</td>\n",
+              "      <td>sweet</td>\n",
+              "      <td>dessert</td>\n",
+              "      <td>Goa</td>\n",
+              "      <td>West</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "<p>255 rows × 9 columns</p>\n",
+              "</div>\n",
+              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-113c5967-9484-46a8-974b-637c7b7a6f1a')\"\n",
+              "              title=\"Convert this dataframe to an interactive table.\"\n",
+              "              style=\"display:none;\">\n",
+              "        \n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "       width=\"24px\">\n",
+              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
+              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
+              "  </svg>\n",
+              "      </button>\n",
+              "      \n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      flex-wrap:wrap;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "      <script>\n",
+              "        const buttonEl =\n",
+              "          document.querySelector('#df-113c5967-9484-46a8-974b-637c7b7a6f1a button.colab-df-convert');\n",
+              "        buttonEl.style.display =\n",
+              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "        async function convertToInteractive(key) {\n",
+              "          const element = document.querySelector('#df-113c5967-9484-46a8-974b-637c7b7a6f1a');\n",
+              "          const dataTable =\n",
+              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                     [key], {});\n",
+              "          if (!dataTable) return;\n",
+              "\n",
+              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "            + ' to learn more about interactive tables.';\n",
+              "          element.innerHTML = '';\n",
+              "          dataTable['output_type'] = 'display_data';\n",
+              "          await google.colab.output.renderOutput(dataTable, element);\n",
+              "          const docLink = document.createElement('div');\n",
+              "          docLink.innerHTML = docLinkHtml;\n",
+              "          element.appendChild(docLink);\n",
+              "        }\n",
+              "      </script>\n",
+              "    </div>\n",
+              "  </div>\n",
+              "  "
+            ],
+            "text/plain": [
+              "               name                                        ingredients  \\\n",
+              "0        Balu shahi                    Maida flour, yogurt, oil, sugar   \n",
+              "1            Boondi                            Gram flour, ghee, sugar   \n",
+              "2    Gajar ka halwa       Carrots, milk, sugar, ghee, cashews, raisins   \n",
+              "3            Ghevar  Flour, ghee, kewra, milk, clarified butter, su...   \n",
+              "4       Gulab jamun  Milk powder, plain flour, baking powder, ghee,...   \n",
+              "..              ...                                                ...   \n",
+              "250       Til Pitha            Glutinous rice, black sesame seeds, gur   \n",
+              "251         Bebinca  Coconut milk, egg yolks, clarified butter, all...   \n",
+              "252          Shufta  Cottage cheese, dry dates, dried rose petals, ...   \n",
+              "253       Mawa Bati  Milk powder, dry fruits, arrowroot powder, all...   \n",
+              "254          Pinaca  Brown rice, fennel seeds, grated coconut, blac...   \n",
+              "\n",
+              "           diet  prep_time  cook_time flavor_profile   course  \\\n",
+              "0    vegetarian         45         25          sweet  dessert   \n",
+              "1    vegetarian         80         30          sweet  dessert   \n",
+              "2    vegetarian         15         60          sweet  dessert   \n",
+              "3    vegetarian         15         30          sweet  dessert   \n",
+              "4    vegetarian         15         40          sweet  dessert   \n",
+              "..          ...        ...        ...            ...      ...   \n",
+              "250  vegetarian          5         30          sweet  dessert   \n",
+              "251  vegetarian         20         60          sweet  dessert   \n",
+              "252  vegetarian         -1         -1          sweet  dessert   \n",
+              "253  vegetarian         20         45          sweet  dessert   \n",
+              "254  vegetarian         -1         -1          sweet  dessert   \n",
+              "\n",
+              "               state      region  \n",
+              "0        West Bengal        East  \n",
+              "1          Rajasthan        West  \n",
+              "2             Punjab       North  \n",
+              "3          Rajasthan        West  \n",
+              "4        West Bengal        East  \n",
+              "..               ...         ...  \n",
+              "250            Assam  North East  \n",
+              "251              Goa        West  \n",
+              "252  Jammu & Kashmir       North  \n",
+              "253   Madhya Pradesh     Central  \n",
+              "254              Goa        West  \n",
+              "\n",
+              "[255 rows x 9 columns]"
+            ]
+          },
+          "execution_count": 8,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "df = pd.read_csv('/content/indian-food-101/indian_food.csv')\n",
+        "df"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 424
+        },
+        "id": "UbsiYdQVaPcx",
+        "outputId": "125ca9ab-5521-443f-cee2-7076b00643c3"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/html": [
+              "\n",
+              "  <div id=\"df-c36f220e-b1a6-45c0-a576-0df52574d19f\">\n",
+              "    <div class=\"colab-df-container\">\n",
+              "      <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>name</th>\n",
+              "      <th>diet</th>\n",
+              "      <th>prep_time</th>\n",
+              "      <th>cook_time</th>\n",
+              "      <th>flavor_profile</th>\n",
+              "      <th>course</th>\n",
+              "      <th>state</th>\n",
+              "      <th>region</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>Balu shahi</td>\n",
+              "      <td>vegetarian</td>\n",
+              "      <td>45</td>\n",
+              "      <td>25</td>\n",
+              "      <td>sweet</td>\n",
+              "      <td>dessert</td>\n",
+              "      <td>West Bengal</td>\n",
+              "      <td>East</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>Boondi</td>\n",
+              "      <td>vegetarian</td>\n",
+              "      <td>80</td>\n",
+              "      <td>30</td>\n",
+              "      <td>sweet</td>\n",
+              "      <td>dessert</td>\n",
+              "      <td>Rajasthan</td>\n",
+              "      <td>West</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>Gajar ka halwa</td>\n",
+              "      <td>vegetarian</td>\n",
+              "      <td>15</td>\n",
+              "      <td>60</td>\n",
+              "      <td>sweet</td>\n",
+              "      <td>dessert</td>\n",
+              "      <td>Punjab</td>\n",
+              "      <td>North</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>Ghevar</td>\n",
+              "      <td>vegetarian</td>\n",
+              "      <td>15</td>\n",
+              "      <td>30</td>\n",
+              "      <td>sweet</td>\n",
+              "      <td>dessert</td>\n",
+              "      <td>Rajasthan</td>\n",
+              "      <td>West</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>Gulab jamun</td>\n",
+              "      <td>vegetarian</td>\n",
+              "      <td>15</td>\n",
+              "      <td>40</td>\n",
+              "      <td>sweet</td>\n",
+              "      <td>dessert</td>\n",
+              "      <td>West Bengal</td>\n",
+              "      <td>East</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>...</th>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>250</th>\n",
+              "      <td>Til Pitha</td>\n",
+              "      <td>vegetarian</td>\n",
+              "      <td>5</td>\n",
+              "      <td>30</td>\n",
+              "      <td>sweet</td>\n",
+              "      <td>dessert</td>\n",
+              "      <td>Assam</td>\n",
+              "      <td>North East</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>251</th>\n",
+              "      <td>Bebinca</td>\n",
+              "      <td>vegetarian</td>\n",
+              "      <td>20</td>\n",
+              "      <td>60</td>\n",
+              "      <td>sweet</td>\n",
+              "      <td>dessert</td>\n",
+              "      <td>Goa</td>\n",
+              "      <td>West</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>252</th>\n",
+              "      <td>Shufta</td>\n",
+              "      <td>vegetarian</td>\n",
+              "      <td>-1</td>\n",
+              "      <td>-1</td>\n",
+              "      <td>sweet</td>\n",
+              "      <td>dessert</td>\n",
+              "      <td>Jammu &amp; Kashmir</td>\n",
+              "      <td>North</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>253</th>\n",
+              "      <td>Mawa Bati</td>\n",
+              "      <td>vegetarian</td>\n",
+              "      <td>20</td>\n",
+              "      <td>45</td>\n",
+              "      <td>sweet</td>\n",
+              "      <td>dessert</td>\n",
+              "      <td>Madhya Pradesh</td>\n",
+              "      <td>Central</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>254</th>\n",
+              "      <td>Pinaca</td>\n",
+              "      <td>vegetarian</td>\n",
+              "      <td>-1</td>\n",
+              "      <td>-1</td>\n",
+              "      <td>sweet</td>\n",
+              "      <td>dessert</td>\n",
+              "      <td>Goa</td>\n",
+              "      <td>West</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "<p>254 rows × 8 columns</p>\n",
+              "</div>\n",
+              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c36f220e-b1a6-45c0-a576-0df52574d19f')\"\n",
+              "              title=\"Convert this dataframe to an interactive table.\"\n",
+              "              style=\"display:none;\">\n",
+              "        \n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "       width=\"24px\">\n",
+              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
+              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
+              "  </svg>\n",
+              "      </button>\n",
+              "      \n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      flex-wrap:wrap;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "      <script>\n",
+              "        const buttonEl =\n",
+              "          document.querySelector('#df-c36f220e-b1a6-45c0-a576-0df52574d19f button.colab-df-convert');\n",
+              "        buttonEl.style.display =\n",
+              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "        async function convertToInteractive(key) {\n",
+              "          const element = document.querySelector('#df-c36f220e-b1a6-45c0-a576-0df52574d19f');\n",
+              "          const dataTable =\n",
+              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                     [key], {});\n",
+              "          if (!dataTable) return;\n",
+              "\n",
+              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "            + ' to learn more about interactive tables.';\n",
+              "          element.innerHTML = '';\n",
+              "          dataTable['output_type'] = 'display_data';\n",
+              "          await google.colab.output.renderOutput(dataTable, element);\n",
+              "          const docLink = document.createElement('div');\n",
+              "          docLink.innerHTML = docLinkHtml;\n",
+              "          element.appendChild(docLink);\n",
+              "        }\n",
+              "      </script>\n",
+              "    </div>\n",
+              "  </div>\n",
+              "  "
+            ],
+            "text/plain": [
+              "               name        diet  prep_time  cook_time flavor_profile   course  \\\n",
+              "0        Balu shahi  vegetarian         45         25          sweet  dessert   \n",
+              "1            Boondi  vegetarian         80         30          sweet  dessert   \n",
+              "2    Gajar ka halwa  vegetarian         15         60          sweet  dessert   \n",
+              "3            Ghevar  vegetarian         15         30          sweet  dessert   \n",
+              "4       Gulab jamun  vegetarian         15         40          sweet  dessert   \n",
+              "..              ...         ...        ...        ...            ...      ...   \n",
+              "250       Til Pitha  vegetarian          5         30          sweet  dessert   \n",
+              "251         Bebinca  vegetarian         20         60          sweet  dessert   \n",
+              "252          Shufta  vegetarian         -1         -1          sweet  dessert   \n",
+              "253       Mawa Bati  vegetarian         20         45          sweet  dessert   \n",
+              "254          Pinaca  vegetarian         -1         -1          sweet  dessert   \n",
+              "\n",
+              "               state      region  \n",
+              "0        West Bengal        East  \n",
+              "1          Rajasthan        West  \n",
+              "2             Punjab       North  \n",
+              "3          Rajasthan        West  \n",
+              "4        West Bengal        East  \n",
+              "..               ...         ...  \n",
+              "250            Assam  North East  \n",
+              "251              Goa        West  \n",
+              "252  Jammu & Kashmir       North  \n",
+              "253   Madhya Pradesh     Central  \n",
+              "254              Goa        West  \n",
+              "\n",
+              "[254 rows x 8 columns]"
+            ]
+          },
+          "execution_count": 9,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "# Droping the missing values and 'ingredients' column\n",
+        "df = df.drop(['ingredients'],axis=1).dropna(axis=0)\n",
+        "df"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 424
+        },
+        "id": "FiTdBvryaRJp",
+        "outputId": "a2fab734-feb1-4aa6-b385-d5e516218861"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/html": [
+              "\n",
+              "  <div id=\"df-3d8b2865-4997-4035-bba8-6a7fc2f9ffdc\">\n",
+              "    <div class=\"colab-df-container\">\n",
+              "      <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>name</th>\n",
+              "      <th>diet</th>\n",
+              "      <th>prep_time</th>\n",
+              "      <th>cook_time</th>\n",
+              "      <th>flavor_profile</th>\n",
+              "      <th>course</th>\n",
+              "      <th>state</th>\n",
+              "      <th>region</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>Balu shahi</td>\n",
+              "      <td>vegetarian</td>\n",
+              "      <td>45</td>\n",
+              "      <td>25</td>\n",
+              "      <td>sweet</td>\n",
+              "      <td>dessert</td>\n",
+              "      <td>West Bengal</td>\n",
+              "      <td>East</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>Boondi</td>\n",
+              "      <td>vegetarian</td>\n",
+              "      <td>80</td>\n",
+              "      <td>30</td>\n",
+              "      <td>sweet</td>\n",
+              "      <td>dessert</td>\n",
+              "      <td>Rajasthan</td>\n",
+              "      <td>West</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>Gajar ka halwa</td>\n",
+              "      <td>vegetarian</td>\n",
+              "      <td>15</td>\n",
+              "      <td>60</td>\n",
+              "      <td>sweet</td>\n",
+              "      <td>dessert</td>\n",
+              "      <td>Punjab</td>\n",
+              "      <td>North</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>Ghevar</td>\n",
+              "      <td>vegetarian</td>\n",
+              "      <td>15</td>\n",
+              "      <td>30</td>\n",
+              "      <td>sweet</td>\n",
+              "      <td>dessert</td>\n",
+              "      <td>Rajasthan</td>\n",
+              "      <td>West</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>Gulab jamun</td>\n",
+              "      <td>vegetarian</td>\n",
+              "      <td>15</td>\n",
+              "      <td>40</td>\n",
+              "      <td>sweet</td>\n",
+              "      <td>dessert</td>\n",
+              "      <td>West Bengal</td>\n",
+              "      <td>East</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>...</th>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>247</th>\n",
+              "      <td>Prawn malai curry</td>\n",
+              "      <td>non vegetarian</td>\n",
+              "      <td>15</td>\n",
+              "      <td>50</td>\n",
+              "      <td>spicy</td>\n",
+              "      <td>main course</td>\n",
+              "      <td>West Bengal</td>\n",
+              "      <td>East</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>249</th>\n",
+              "      <td>Shukto</td>\n",
+              "      <td>vegetarian</td>\n",
+              "      <td>10</td>\n",
+              "      <td>20</td>\n",
+              "      <td>spicy</td>\n",
+              "      <td>main course</td>\n",
+              "      <td>West Bengal</td>\n",
+              "      <td>East</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>250</th>\n",
+              "      <td>Til Pitha</td>\n",
+              "      <td>vegetarian</td>\n",
+              "      <td>5</td>\n",
+              "      <td>30</td>\n",
+              "      <td>sweet</td>\n",
+              "      <td>dessert</td>\n",
+              "      <td>Assam</td>\n",
+              "      <td>North East</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>251</th>\n",
+              "      <td>Bebinca</td>\n",
+              "      <td>vegetarian</td>\n",
+              "      <td>20</td>\n",
+              "      <td>60</td>\n",
+              "      <td>sweet</td>\n",
+              "      <td>dessert</td>\n",
+              "      <td>Goa</td>\n",
+              "      <td>West</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>253</th>\n",
+              "      <td>Mawa Bati</td>\n",
+              "      <td>vegetarian</td>\n",
+              "      <td>20</td>\n",
+              "      <td>45</td>\n",
+              "      <td>sweet</td>\n",
+              "      <td>dessert</td>\n",
+              "      <td>Madhya Pradesh</td>\n",
+              "      <td>Central</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "<p>180 rows × 8 columns</p>\n",
+              "</div>\n",
+              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-3d8b2865-4997-4035-bba8-6a7fc2f9ffdc')\"\n",
+              "              title=\"Convert this dataframe to an interactive table.\"\n",
+              "              style=\"display:none;\">\n",
+              "        \n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "       width=\"24px\">\n",
+              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
+              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
+              "  </svg>\n",
+              "      </button>\n",
+              "      \n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      flex-wrap:wrap;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "      <script>\n",
+              "        const buttonEl =\n",
+              "          document.querySelector('#df-3d8b2865-4997-4035-bba8-6a7fc2f9ffdc button.colab-df-convert');\n",
+              "        buttonEl.style.display =\n",
+              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "        async function convertToInteractive(key) {\n",
+              "          const element = document.querySelector('#df-3d8b2865-4997-4035-bba8-6a7fc2f9ffdc');\n",
+              "          const dataTable =\n",
+              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                     [key], {});\n",
+              "          if (!dataTable) return;\n",
+              "\n",
+              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "            + ' to learn more about interactive tables.';\n",
+              "          element.innerHTML = '';\n",
+              "          dataTable['output_type'] = 'display_data';\n",
+              "          await google.colab.output.renderOutput(dataTable, element);\n",
+              "          const docLink = document.createElement('div');\n",
+              "          docLink.innerHTML = docLinkHtml;\n",
+              "          element.appendChild(docLink);\n",
+              "        }\n",
+              "      </script>\n",
+              "    </div>\n",
+              "  </div>\n",
+              "  "
+            ],
+            "text/plain": [
+              "                  name            diet  prep_time  cook_time flavor_profile  \\\n",
+              "0           Balu shahi      vegetarian         45         25          sweet   \n",
+              "1               Boondi      vegetarian         80         30          sweet   \n",
+              "2       Gajar ka halwa      vegetarian         15         60          sweet   \n",
+              "3               Ghevar      vegetarian         15         30          sweet   \n",
+              "4          Gulab jamun      vegetarian         15         40          sweet   \n",
+              "..                 ...             ...        ...        ...            ...   \n",
+              "247  Prawn malai curry  non vegetarian         15         50          spicy   \n",
+              "249             Shukto      vegetarian         10         20          spicy   \n",
+              "250          Til Pitha      vegetarian          5         30          sweet   \n",
+              "251            Bebinca      vegetarian         20         60          sweet   \n",
+              "253          Mawa Bati      vegetarian         20         45          sweet   \n",
+              "\n",
+              "          course           state      region  \n",
+              "0        dessert     West Bengal        East  \n",
+              "1        dessert       Rajasthan        West  \n",
+              "2        dessert          Punjab       North  \n",
+              "3        dessert       Rajasthan        West  \n",
+              "4        dessert     West Bengal        East  \n",
+              "..           ...             ...         ...  \n",
+              "247  main course     West Bengal        East  \n",
+              "249  main course     West Bengal        East  \n",
+              "250      dessert           Assam  North East  \n",
+              "251      dessert             Goa        West  \n",
+              "253      dessert  Madhya Pradesh     Central  \n",
+              "\n",
+              "[180 rows x 8 columns]"
+            ]
+          },
+          "execution_count": 10,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "# Removing data with invalid entries (-1)\n",
+        "df1= df[df.prep_time !=-1]\n",
+        "df1= df1[df1.cook_time !=-1]\n",
+        "df1= df1[df1.flavor_profile !='-1']\n",
+        "df1= df1[df1.state !='-1']\n",
+        "df1= df1[df1.region !='-1']\n",
+        "df1"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "B5_EF1Brc7ON"
+      },
+      "source": [
+        "**Exploratory Analysis and Visualization**\n",
+        "\n",
+        "Importing libraries for visualization i.e matplotlib, seaborn, etc."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "_XAESJH5aUUJ"
+      },
+      "outputs": [],
+      "source": [
+        "import seaborn as sns\n",
+        "import matplotlib\n",
+        "import matplotlib.pyplot as plt\n",
+        "%matplotlib inline\n",
+        "\n",
+        "sns.set_style('darkgrid')\n",
+        "matplotlib.rcParams['font.size'] = 14\n",
+        "matplotlib.rcParams['figure.figsize'] = (9, 5)\n",
+        "matplotlib.rcParams['figure.facecolor'] = '#00000000'"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "a5bQ-Or1aWQB",
+        "outputId": "05d403ac-97ba-4072-c2cd-1f771a3b480f"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "<class 'pandas.core.frame.DataFrame'>\n",
+            "Int64Index: 180 entries, 0 to 253\n",
+            "Data columns (total 8 columns):\n",
+            " #   Column          Non-Null Count  Dtype \n",
+            "---  ------          --------------  ----- \n",
+            " 0   name            180 non-null    object\n",
+            " 1   diet            180 non-null    object\n",
+            " 2   prep_time       180 non-null    int64 \n",
+            " 3   cook_time       180 non-null    int64 \n",
+            " 4   flavor_profile  180 non-null    object\n",
+            " 5   course          180 non-null    object\n",
+            " 6   state           180 non-null    object\n",
+            " 7   region          180 non-null    object\n",
+            "dtypes: int64(2), object(6)\n",
+            "memory usage: 12.7+ KB\n"
+          ]
+        }
+      ],
+      "source": [
+        "df1.info()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 300
+        },
+        "id": "2fg1TTehaX05",
+        "outputId": "e0585b07-479b-4c70-9c21-4ed5a02cf36f"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/html": [
+              "\n",
+              "  <div id=\"df-828b0926-2bc9-465f-803e-00e225ec923d\">\n",
+              "    <div class=\"colab-df-container\">\n",
+              "      <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>prep_time</th>\n",
+              "      <th>cook_time</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>count</th>\n",
+              "      <td>180.000000</td>\n",
+              "      <td>180.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mean</th>\n",
+              "      <td>30.705556</td>\n",
+              "      <td>40.366667</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>64.158036</td>\n",
+              "      <td>54.377215</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>5.000000</td>\n",
+              "      <td>5.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>10.000000</td>\n",
+              "      <td>25.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>10.000000</td>\n",
+              "      <td>30.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>20.000000</td>\n",
+              "      <td>45.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>500.000000</td>\n",
+              "      <td>720.000000</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-828b0926-2bc9-465f-803e-00e225ec923d')\"\n",
+              "              title=\"Convert this dataframe to an interactive table.\"\n",
+              "              style=\"display:none;\">\n",
+              "        \n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "       width=\"24px\">\n",
+              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
+              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
+              "  </svg>\n",
+              "      </button>\n",
+              "      \n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      flex-wrap:wrap;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "      <script>\n",
+              "        const buttonEl =\n",
+              "          document.querySelector('#df-828b0926-2bc9-465f-803e-00e225ec923d button.colab-df-convert');\n",
+              "        buttonEl.style.display =\n",
+              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "        async function convertToInteractive(key) {\n",
+              "          const element = document.querySelector('#df-828b0926-2bc9-465f-803e-00e225ec923d');\n",
+              "          const dataTable =\n",
+              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                     [key], {});\n",
+              "          if (!dataTable) return;\n",
+              "\n",
+              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "            + ' to learn more about interactive tables.';\n",
+              "          element.innerHTML = '';\n",
+              "          dataTable['output_type'] = 'display_data';\n",
+              "          await google.colab.output.renderOutput(dataTable, element);\n",
+              "          const docLink = document.createElement('div');\n",
+              "          docLink.innerHTML = docLinkHtml;\n",
+              "          element.appendChild(docLink);\n",
+              "        }\n",
+              "      </script>\n",
+              "    </div>\n",
+              "  </div>\n",
+              "  "
+            ],
+            "text/plain": [
+              "        prep_time   cook_time\n",
+              "count  180.000000  180.000000\n",
+              "mean    30.705556   40.366667\n",
+              "std     64.158036   54.377215\n",
+              "min      5.000000    5.000000\n",
+              "25%     10.000000   25.000000\n",
+              "50%     10.000000   30.000000\n",
+              "75%     20.000000   45.000000\n",
+              "max    500.000000  720.000000"
+            ]
+          },
+          "execution_count": 13,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "df1.describe()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "1aCtO9SyabDJ",
+        "outputId": "1468e2d2-0814-4427-ac44-333a7c3cacdc"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "main course    83\n",
+              "dessert        70\n",
+              "snack          25\n",
+              "starter         2\n",
+              "Name: course, dtype: int64"
+            ]
+          },
+          "execution_count": 14,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "# Coursewise No. of dishes\n",
+        "course_counts = df1.course.value_counts()\n",
+        "course_counts"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 394
+        },
+        "id": "g-JERJLdacMK",
+        "outputId": "ef970266-0089-4133-9520-a89a85e58611"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "<matplotlib.legend.Legend at 0x7f97ae854650>"
+            ]
+          },
+          "execution_count": 18,
+          "metadata": {},
+          "output_type": "execute_result"
+        },
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 864x432 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "plt.figure(figsize=(12,6))\n",
+        "plt.title('Coursewise distribution of Indian dishes')\n",
+        "plt.pie(course_counts, labels=course_counts.index, autopct='%1.1f%%', startangle=180);\n",
+        "plt.legend()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "pYWacJWkdKxt"
+      },
+      "source": [
+        "The pie chart clearly shows the coursewise distribution of Indian dishes.\n",
+        "\n",
+        "About 46% of Indian dishes comes under main course which is the highest among all courses.\n",
+        "\n",
+        "And only 1% of Indian dishes comes under starter which is the least among all courses."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "X7_t-YFOae8H",
+        "outputId": "02f62915-cdea-4a0a-b1ab-b3ebd6375557"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "spicy     102\n",
+              "sweet      73\n",
+              "bitter      4\n",
+              "sour        1\n",
+              "Name: flavor_profile, dtype: int64"
+            ]
+          },
+          "execution_count": 16,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "# Flavorwise No. of dishes\n",
+        "flavor_counts = df1.flavor_profile.value_counts()\n",
+        "flavor_counts"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 394
+        },
+        "id": "jmPAd0yWafpi",
+        "outputId": "42bc7452-f1df-4c80-b473-36b703f5aa86"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "<matplotlib.legend.Legend at 0x7f97ae8d7e90>"
+            ]
+          },
+          "execution_count": 17,
+          "metadata": {},
+          "output_type": "execute_result"
+        },
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 864x432 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "plt.figure(figsize=(12,6))\n",
+        "plt.title('Flavorwise distribution of Indian dishes')\n",
+        "plt.pie(flavor_counts, labels=flavor_counts.index, autopct='%1.1f%%', startangle=180);\n",
+        "plt.legend()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "sPoYjf-IdRca"
+      },
+      "source": [
+        "The pie chart clearly shows the flavorwise distribution of Indian dishes.\n",
+        "\n",
+        "About 56.7% of Indian dishes are spicy which is the highest among all flavors.\n",
+        "\n",
+        "And only 0.6% of Indian dishes are sour which is the least among all flavors."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "Bc68SfqXasgO",
+        "outputId": "3f5fcf64-0b33-4e68-8139-cb57e1419668"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "vegetarian        163\n",
+              "non vegetarian     17\n",
+              "Name: diet, dtype: int64"
+            ]
+          },
+          "execution_count": 19,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "# Calculating No. of veg & Non-veg dishes\n",
+        "diet_counts = df1.diet.value_counts()\n",
+        "diet_counts"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 394
+        },
+        "id": "VBFAm3p7auRe",
+        "outputId": "e7418c6a-4892-4162-8335-fe63aede6117"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "<matplotlib.legend.Legend at 0x7f97ae7c4b90>"
+            ]
+          },
+          "execution_count": 20,
+          "metadata": {},
+          "output_type": "execute_result"
+        },
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 864x432 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "# Calculating No. of veg & Non-veg dishes\n",
+        "plt.figure(figsize=(12,6))\n",
+        "plt.title('Dietwise distribution of Indian dishes')\n",
+        "plt.pie(diet_counts, labels=diet_counts.index, autopct='%1.1f%%', startangle=360);\n",
+        "plt.legend()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "z5DkwdcDdWPR"
+      },
+      "source": [
+        "The pie chart clearly shows the dietwise distribution of Indian dishes.\n",
+        "\n",
+        "About 91% of Indian dishes are veg.\n",
+        "\n",
+        "And only 9% of Indian dishes are Non-veg."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 456
+        },
+        "id": "9rFyMqeKavwf",
+        "outputId": "d716f7e2-f6fc-4ddf-cd18-9e6fb537c2cb"
+      },
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 648x360 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "needs_background": "light"
+          },
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "sns.countplot(x=df1.state)\n",
+        "plt.xticks(rotation=85);\n",
+        "plt.title('Statewise number of Indian dishes')\n",
+        "plt.xlabel(None);"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "ICbbSZrNdcuJ"
+      },
+      "source": [
+        "From this graph we can clearly see the which state of India has more number of dishes.\n",
+        "\n",
+        "Punjab has the highest number of dishes about 30 dishes.\n",
+        "\n",
+        "Closely followed by Gujarat having the second highest number of dishes about 29 dishes.\n",
+        "\n",
+        "And states like Nagaland, Jammu & Kashmir, Uttarakhand, Manipur, Chhattisgarh have very few dishes."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 362
+        },
+        "id": "cDt91ykzaxU_",
+        "outputId": "ae6736be-653f-4376-8a1a-5e800126a5f2"
+      },
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 648x360 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "needs_background": "light"
+          },
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "sns.countplot(y=df1.region)\n",
+        "plt.title('Regionwise number of Indian dishes')\n",
+        "plt.ylabel(None);"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "LGGHHN7odiU_"
+      },
+      "source": [
+        "From this graph we can clearly see the which region of India has more number of dishes.\n",
+        "\n",
+        "West India has the highest number of dishes about 60 dishes.\n",
+        "\n",
+        "Followed by North India having the second highest number of dishes about 45 dishes.\n",
+        "\n",
+        "And Central India has the least number of dishes about 3 dishes."
+      ]
+    }
+  ],
+  "metadata": {
+    "colab": {
+      "collapsed_sections": [],
+      "provenance": []
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "name": "python3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}