Chat with us, powered by LiveChat Does price positively effect customer satisfaction? Analysis Requirements (Jupyter Notebook) Introduce the problem and define key terms 5-10 sentences At least one c - EssayAbode

Does price positively effect customer satisfaction? Analysis Requirements (Jupyter Notebook) Introduce the problem and define key terms 5-10 sentences At least one c

Now it's time to answer your business question:

Does price positively effect customer satisfaction?

Analysis Requirements (Jupyter Notebook)

  • Introduce the problem and define key terms
    • 5-10 sentences
    • At least one credible source for each key term defined
  • Answer the business question
    • 5-10 sentences
    • Make sure your results are statistically significant
  • Provide your top two actionable insights
    • 5-10 sentences each
    • Provide at least one credible source per insight.
    • Make sure to go beyond the numbers. Note that the company is likely to already be taking advantage of common metrics such as correlations and is expecting a deeper level of analysis.
  • Use markdown to explain the rest of your analysis
    • 250-450 words
    • Remember that markdown is used to explain what you, the analyst, has found important through the code. Code comments are used to explain the technical aspects of the code.

SQL Requirements

  • Provide the SQL queries needed to:
    • explore the data leading up to the creation of your final dataset
    • develop your final dataset (this is what will be exported into Excel and then read into Python)
  • Make sure to include a USE statement and ample comments throughout your code.
  • Do not use AI to generate any of your SQL code.

Python Requirements

  • Your code must generate the following:
    • Descriptive statistics
    • Frequency tables
    • Correlation
    • 3-5 well-designed, highly relevant data visualizations (scatterplots, boxplots, etc.)
  • Make sure to avoid data dumping:
    • Remove any outputs/visuals that do not directly support your insights
    • Limit your tabular outputs
  • Do not use AI to generate any of your Python code.

Tips

  • To get your final dataset from SQL to Python, you may export the data from SQL into an Excel file and then imported into Python with pd.read_excel().
  • Avoid writing about what you did. Your stakeholders will assume that you took proper steps to analyze the data and do not have the bandwidth to read through your process. They are more interested in your answer to the business question, as well as your top two actionable insights.
  • Note that your stakeholders will start asking questions about the validity of your results if your insights stray from the SQL queries/Python code you provide.
  • Additional files (Excel, etc.) will not be assessed.

Deliverables

1. Submit a Jupyter Notebook in the following two formats:

  • Jupyter Notebook (.ipynb format)
  • HTML page, converted directly from the Jupyter Notebook interface (.html format)

2. Submit your SQL queries in the following two formats:

  • SQL script (.sql format)
  • Text file (.txt format)

DROP DATABASE IF EXISTS weddings; CREATE DATABASE IF NOT EXISTS weddings; USE weddings; — Departments CREATE TABLE departments ( function_id CHAR(2) NOT NULL, functions VARCHAR(16), PRIMARY KEY (function_id) ); — prices Table CREATE TABLE prices ( price_id CHAR(1) NOT NULL, affordability VARCHAR(11) NOT NULL, PRIMARY KEY(price_id) ); — cities Table CREATE TABLE cities( city_id CHAR(6) NOT NULL, city VARCHAR(15), PRIMARY KEY (city_id) ); — Vendors Table CREATE TABLE vendors ( vendor_id CHAR(5) NOT NULL, function_id CHAR(2) NOT NULL, vendor VARCHAR(75) NOT NULL, city_id CHAR(6) NOT NULL, price_id CHAR(1) NOT NULL, reviews INT(4) NOT NULL, avg_stars FLOAT(2,1) NOT NULL, PRIMARY KEY (vendor_id), FOREIGN KEY (function_id) REFERENCES departments(function_id), FOREIGN KEY (city_id) REFERENCES cities(city_id), FOREIGN KEY (price_id) REFERENCES prices(price_id) ); — companies Table CREATE TABLE companies ( cmp_id CHAR(5) NOT NULL, company VARCHAR(75) NOT NULL, PRIMARY KEY(cmp_id) ); — store_cmp Table CREATE TABLE store_cmp ( store_id CHAR(5) NOT NULL, cmp_id CHAR(5) NOT NULL, PRIMARY KEY (store_id), FOREIGN KEY (store_id) REFERENCES vendors(vendor_id), FOREIGN KEY (cmp_id) REFERENCES companies(cmp_id) ); — Sources CREATE TABLE sources ( url_id CHAR(5) NOT NULL, url VARCHAR(150), PRIMARY KEY(url_id), FOREIGN KEY(url_id) REFERENCES vendors(vendor_id) ); — planner_prices Table CREATE TABLE planner_prices( wed_id CHAR(5) NOT NULL, hourly BOOL NOT NULL, daily FLOAT(7,2), `partial` FLOAT(7,2), `full` FLOAT(7,2), PRIMARY KEY(wed_id), FOREIGN KEY(wed_id) REFERENCES vendors(vendor_id) ); — photo_prices Table CREATE TABLE photo_prices ( pho_id CHAR(5) NOT NULL, is_photographer BOOLEAN NOT NULL, package_price FLOAT(5,2), PRIMARY KEY (pho_id), FOREIGN KEY (pho_id) REFERENCES vendors(vendor_id) ); — music_type Table CREATE TABLE music_type ( type_id CHAR(1), `type` VARCHAR(22), PRIMARY KEY(type_id) ); — music_match Table CREATE TABLE music_match ( mus_id CHAR(5), type_id CHAR(1), PRIMARY KEY(mus_id), FOREIGN KEY (mus_id) REFERENCES vendors(vendor_id), FOREIGN KEY (type_id) REFERENCES music_type(type_id) ); — addresses Table CREATE TABLE addresses( address_id CHAR(5) NOT NULL, address VARCHAR(50), PRIMARY KEY (address_id), FOREIGN KEY (address_id) REFERENCES vendors(vendor_id) ); — tasting_cities Table CREATE TABLE tasting_cities ( cat_id CHAR(5) NOT NULL, city_id CHAR(6) NOT NULL, PRIMARY KEY (cat_id), FOREIGN KEY(cat_id) REFERENCES vendors(vendor_id), FOREIGN KEY(city_id) REFERENCES cities(city_id) ); — hair_experience Table CREATE TABLE hair_experience ( hai_id CHAR(5) NOT NULL, years INT(2) NOT NULL, PRIMARY KEY(hai_id), FOREIGN KEY (hai_id) REFERENCES vendors(vendor_id) ); — hair_services Table CREATE TABLE hair_services ( hai_id CHAR(5) NOT NULL, hair BOOLEAN NOT NULL, makeup BOOLEAN NOT NULL, pre_wedding BOOLEAN NOT NULL, PRIMARY KEY(hai_id), FOREIGN KEY (hai_id) REFERENCES vendors(vendor_id) ); — catering_services Table CREATE TABLE catering_services ( cat_id CHAR(5) NOT NULL, buffet BOOL NOT NULL, plate BOOL NOT NULL, PRIMARY KEY (cat_id), FOREIGN KEY (cat_id) REFERENCES vendors(vendor_id) ); — rental_services Table CREATE TABLE rental_services ( ren_id CHAR(5) NOT NULL, decor BOOLEAN NOT NULL, heating BOOLEAN NOT NULL, lighting BOOLEAN NOT NULL, linen BOOLEAN NOT NULL, lounge BOOLEAN NOT NULL, restroom BOOLEAN NOT NULL, shade BOOLEAN NOT NULL, speaker BOOLEAN NOT NULL, stage BOOLEAN NOT NULL, table_chair BOOLEAN NOT NULL, tableware BOOLEAN NOT NULL, tent BOOLEAN NOT NULL, PRIMARY KEY (ren_id), FOREIGN KEY (ren_id) REFERENCES vendors(vendor_id) ); — cake_services Table CREATE TABLE cake_services ( cak_id CHAR(5) NOT NULL, buffet_style BOOLEAN NOT NULL, cake_accessories BOOLEAN NOT NULL, cake_cutting_and_serving BOOLEAN NOT NULL, cake_delivery_and_setup BOOLEAN NOT NULL, cake_serving_sets BOOLEAN NOT NULL, cake_stands BOOLEAN NOT NULL, cake_table_decorations BOOLEAN NOT NULL, cake_tastings BOOLEAN NOT NULL, cake_toppers BOOLEAN NOT NULL, consultations BOOLEAN NOT NULL, cupcakes BOOLEAN NOT NULL, custom_orders BOOLEAN NOT NULL, groom_cakes BOOLEAN NOT NULL, other_desserts BOOLEAN NOT NULL, PRIMARY KEY(cak_id), FOREIGN KEY(cak_id) REFERENCES vendors(vendor_id) ); — cakes Table CREATE TABLE cakes ( cak_id CHAR(5) NOT NULL, almond BOOL NOT NULL, apple BOOL NOT NULL, carrot BOOL NOT NULL, champagne BOOL NOT NULL, cheese BOOL NOT NULL, chocolate BOOL NOT NULL, clementine BOOL NOT NULL, faux_display BOOL NOT NULL, fruit BOOL NOT NULL, lemon BOOL NOT NULL, red_velvet BOOL NOT NULL, sculpture BOOL NOT NULL, tower BOOL NOT NULL, vanilla BOOL NOT NULL, white BOOL NOT NULL, PRIMARY KEY(cak_id), FOREIGN KEY(cak_id) REFERENCES vendors(vendor_id) ); — diets Table CREATE TABLE diets ( diet_id CHAR(5) NOT NULL, dairy_free BOOL NOT NULL, gluten_free BOOL NOT NULL, halal BOOL NOT NULL, keto BOOL NOT NULL, kosher BOOL NOT NULL, nut_free BOOL NOT NULL, organic BOOL NOT NULL, spicy_cube BOOL NOT NULL, sugar_free BOOL NOT NULL, vegan BOOL NOT NULL, vegetarian BOOL NOT NULL, PRIMARY KEY(diet_id), FOREIGN KEY(diet_id) REFERENCES vendors(vendor_id) ); — invitations Table CREATE TABLE invitations ( inv_id CHAR(5) NOT NULL, min_quantity INT(5), PRIMARY KEY (inv_id), FOREIGN KEY (inv_id) REFERENCES vendors(vendor_id) ); — venues Table CREATE TABLE venues( ven_id CHAR(5) NOT NULL, ballroom BOOL NOT NULL, barn BOOL NOT NULL, club_house BOOL NOT NULL, country_club BOOL NOT NULL, gallery BOOL NOT NULL, garden BOOL NOT NULL, golf_club BOOL NOT NULL, hotel BOOL NOT NULL, mansion BOOL NOT NULL, museum BOOL NOT NULL, private_club BOOL NOT NULL, private_estate BOOL NOT NULL, terrace BOOL NOT NULL, winery BOOL NOT NULL, PRIMARY KEY(ven_id), FOREIGN KEY(ven_id) REFERENCES vendors(vendor_id) ); — venue_capacities Table CREATE TABLE venue_capacities ( ven_id CHAR(5) NOT NULL, capacity INT(5), PRIMARY KEY(ven_id), FOREIGN KEY(ven_id) REFERENCES vendors(vendor_id) ); — venue_location Table CREATE TABLE venue_locations ( ven_id CHAR(5) NOT NULL, indoor BOOLEAN NOT NULL, outdoor BOOLEAN NOT NULL, PRIMARY KEY(ven_id), FOREIGN KEY (ven_id) REFERENCES vendors(vendor_id) ); — INSERT INTO departments (function_id, functions) VALUES INSERT INTO departments (function_id, functions) VALUES ('01', 'flowers'); INSERT INTO departments (function_id, functions) VALUES ('02', 'venues'); INSERT INTO departments (function_id, functions) VALUES ('03', 'music'); INSERT INTO departments (function_id, functions) VALUES ('04', 'jewelry'); INSERT INTO departments (function_id, functions) VALUES ('05', 'photo and video'); INSERT INTO departments (function_id, functions) VALUES ('06', 'hair and makeup'); INSERT INTO departments (function_id, functions) VALUES ('07', 'dress atire'); INSERT INTO departments (function_id, functions) VALUES ('08', 'catering'); INSERT INTO departments (function_id, functions) VALUES ('09', 'rentals'); INSERT INTO departments (function_id, functions) VALUES ('10', 'invitations'); INSERT INTO departments (function_id, functions) VALUES ('11', 'cake'); INSERT INTO departments (function_id, functions) VALUES ('12', 'wedding planners'); — filling prices table INSERT INTO prices (price_id, affordability) VALUES ('1', 'inexpensive'), ('2', 'affordable'), ('3', 'moderate'), ('4', 'luxury'); — filling cities INSERT INTO cities VALUES ('city00', ''); INSERT INTO cities VALUES ('city01', 'acampo'); INSERT INTO cities VALUES ('city02', 'alameda'); INSERT INTO cities VALUES ('city03', 'alamo'); INSERT INTO cities VALUES ('city04', 'antioch'); INSERT INTO cities VALUES ('city05', 'berkeley'); INSERT INTO cities VALUES ('city06', 'brentwood'); INSERT INTO cities VALUES ('city07', 'brisbane'); INSERT INTO cities VALUES ('city08', 'burlingame'); INSERT INTO cities VALUES ('city09', 'campbell'); INSERT INTO cities VALUES ('city10', 'castro calley'); INSERT INTO cities VALUES ('city11', 'concord'); INSERT INTO cities VALUES ('city12', 'contra costa'); INSERT INTO cities VALUES ('city13', 'cupertino'); INSERT INTO cities VALUES ('city14', 'daly city'); INSERT INTO cities VALUES ('city15', 'danville'); INSERT INTO cities VALUES ('city16', 'dublin'); INSERT INTO cities VALUES ('city17', 'emeryville'); INSERT INTO cities VALUES ('city18', 'fairfax'); INSERT INTO cities VALUES ('city19', 'foresthill'); INSERT INTO cities VALUES ('city20', 'fremont'); INSERT INTO cities VALUES ('city21', 'gilroy'); INSERT INTO cities VALUES ('city22', 'half moon bay'); INSERT INTO cities VALUES ('city23', 'harbor village'); INSERT INTO cities VALUES ('city24', 'hayward'); INSERT INTO cities VALUES ('city25', 'hollister'); INSERT INTO cities VALUES ('city26', 'la honda'); INSERT INTO cities VALUES ('city27', 'lafayette'); INSERT INTO cities VALUES ('city28', 'lakeshore'); INSERT INTO cities VALUES ('city29', 'livermore'); INSERT INTO cities VALUES ('city30', 'los altos'); INSERT INTO cities VALUES ('city31', 'los gatos'); INSERT INTO cities VALUES ('city32', 'marin'); INSERT INTO cities VALUES ('city33', 'martinez'); INSERT INTO cities VALUES ('city34', 'menlo park'); INSERT INTO cities VALUES ('city35', 'millbrae'); INSERT INTO cities VALUES ('city36', 'milpitas'); INSERT INTO cities VALUES ('city37', 'modesto'); INSERT INTO cities VALUES ('city38', 'monterey'); INSERT INTO cities VALUES ('city39', 'mountain view'); INSERT INTO cities VALUES ('city40', 'napa'); INSERT INTO cities VALUES ('city41', 'newark'); INSERT INTO cities VALUES ('city42', 'nicasio'); INSERT INTO cities VALUES ('city43', 'north oakland'); INSERT INTO cities VALUES ('city44', 'novato'); INSERT INTO cities VALUES ('city45', 'oakland'); INSERT INTO cities VALUES ('city46', 'pacifica'); INSERT INTO cities VALUES ('city47', 'palo alto'); INSERT INTO cities VALUES ('city48', 'pinole'); INSERT INTO cities VALUES ('city49', 'pittsburg'); INSERT INTO cities VALUES ('city50', 'pleasant hill'); INSERT INTO cities VALUES ('city51', 'pleasanton'); INSERT INTO cities VALUES ('city52', 'redwood'); INSERT INTO cities VALUES ('city53', 'roseville'); INSERT INTO cities VALUES ('city54', 'san bruno'); INSERT INTO cities VALUES ('city55', 'san carlos'); INSERT INTO cities VALUES ('city56', 'san francisco'); INSERT INTO cities VALUES ('city57', 'san jose'); INSERT INTO cities VALUES ('city58', 'san leandro'); INSERT INTO cities VALUES ('city59', 'san mateo'); INSERT INTO cities VALUES ('city60', 'san pablo'); INSERT INTO cities VALUES ('city61', 'san rafael'); INSERT INTO cities VALUES ('city62', 'san ramon'); INSERT INTO cities VALUES ('city63', 'santa clara'); INSERT INTO cities VALUES ('city64', 'santa rosa'); INSERT INTO cities VALUES ('city65', 'saratoga'); INSERT INTO cities VALUES ('city66', 'sausalito'); INSERT INTO cities VALUES ('city67', 'solana beach'); INSERT INTO cities VALUES ('city68', 'sonoma'); INSERT INTO cities VALUES ('city69', 'sunnyvale'); INSERT INTO cities VALUES ('city70', 'sunol'); INSERT INTO cities VALUES ('city71', 'telegraph hill'); INSERT INTO cities VALUES ('city72', 'walnut creek'); INSERT INTO cities VALUES ('city73', 'winters'); INSERT INTO cities VALUES ('city74', 'woodside'); — Filling vendors table INSERT INTO vendors (vendor_id, function_id, vendor, city_id, price_id, reviews, avg_stars) VALUES ('flo01', '01', 'dannas flowers', 'city22', '2', 89, 3.9), ('flo02', '01', 'the flower girl', 'city20', '2', 55, 4.2), ('flo03', '01', 'finer flora', 'city19', '2', 39, 4.2), ('flo04', '01', 'the botany shop florist', 'city18', '2', 54, 4.2), ('flo05', '01', 'fresh ideas flower cmpany', 'city37', '2', 98, 4.4), ('flo06', '01', 'edmond’s plaza floris', 'city19', '2', 291, 4.3), ('flo07', '01', 'wisteria rockbridge', 'city03', '2', 73, 4.5), ('flo08', '01', 'jorys flowers', 'city11', '2', 244, 4.3), ('flo09', '01', 'petals by cary', 'city11', '2', 34, 4.9), ('flo10', '01', 'fresh petals', 'city33', '1', 32, 5.0), ('flo11', '01', 'the love stop 2', 'city45', '2', 11, 4.6), ('flo12', '01', 'fringe flower cmpany', 'city26', '3', 65, 4.7), ('flo13', '01', 'pico soriano designs', 'city04', '2', 40, 5.0), ('flo14', '01', 'dandelion flowers', 'city02', '2', 203, 4.7), ('flo15', '01', 'nikkibana floral design', 'city22', '2', 64, 4.9), ('flo16', '01', 'belle- flower', 'city17', '2', 79, 5.0), ('flo17', '01', 'utsuwa floral design', 'city03', '2', 328, 5.0), ('flo18', '01', 'anns petals', 'city03', '2', 418, 4.9), ('flo19', '01', 'half moon flowers', 'city22', '2', 38, 4.9), ('flo20', '01', 'apple blossom florist', 'city45', '2', 113, 4.6), ('flo21', '01', 'petals and decor', 'city56', '2', 162, 4.8), ('flo22', '01', 'the floral loft', 'city56', '2', 132, 4.8), ('flo23', '01', 'stanford floral design', 'city47', '2', 135, 4.8), ('flo24', '01', 'flowers by sophia', 'city10', '2', 220, 4.7), ('flo25', '01', 'my wedding sense', 'city20', '2', 24, 5.0), ('flo26', '01', 'huyen flowers', 'city24', '2', 39, 5.0), ('flo27', '01', 'flowers by myrna', 'city45', '2', 63, 4.8), ('flo28', '01', 'urban flora', 'city57', '1', 319, 4.7), ('flo29', '01', 'flowers by janet', 'city57', '1', 149, 4.9), ('flo30', '01', 'vo floral design', 'city58', '2', 131, 4.7), ('flo31', '01', 'brother and sisters flower shop', 'city45', '2', 91, 4.7), ('flo32', '01', 'dream flowers', 'city58', '2', 137, 4.9), ('ven01', '02', 'viaggio estate and winery', 'city01', '1', 22, 4.1), ('ven02', '02', 'fort mason center for arts and culture', 'city56', '1', 17, 4.5), ('ven03', '02', 'curdiodyssey', 'city59', '1', 12, 4.9), ('ven04', '02', 'golden gate club at the presidio', 'city56', '2', 11, 4.5), ('ven05', '02', 'the terrace room at lake merritt', 'city45', '2', 32, 4.9), ('ven06', '02', 'the university club of san francisco', 'city56', '2', 51, 5.0), ('ven07', '02', 'doubletree by hilton hotel berkley marina', 'city05', '2', 20, 4.8), ('ven08', '02', 'chabot space and science center', 'city45', '2', 30, 5.0), ('ven09', '02', 'nella terra cellars', 'city70', '2', 105, 4.9), ('ven10', '02', 'eagle ridge by wedgewood weddings', 'city21', '2', 87, 4.8), ('ven11', '02', 'the mountain terrace', 'city74', '2', 55, 4.6), ('ven12', '02', 'stonetree by wedgewood', 'city44', '2', 53, 4.9), ('ven13', '02', 'the ranch at silvercreek by wedgewood weddings', 'city57', '2', 28, 3.9), ('ven14', '02', 'léal vineyards', 'city25', '2', 24, 4.8), ('ven15', '02', 'rancho nicasio', 'city42', '2', 71, 5.0), ('ven16', '02', 'the bridges golf club', 'city62', '3', 93, 4.8), ('ven17', '02', 'the westin st francis san francisco on union square', 'city56', '3', 77, 5.0), ('ven18', '02', 'the club at ruby hill', 'city51', '3', 108, 4.8), ('ven19', '02', 'the bridges golf club', 'city62', '2', 93, 4.8), ('ven20', '02', 'palm event center in the vineyard', 'city51', '3', 78, 4.9), ('ven21', '02', 'lomas santa fe country club', 'city67', '3', 75, 5.0), ('ven22', '02', 'casa real at ruby hill winery', 'city51', '3', 59, 5.0), ('ven23', '02', 'stemple creek ranch', 'city32', '3', 10, 5.0), ('ven24', '02', 'nestldown', 'city31', '3', 58, 4.5), ('ven25', '02', 'purple orchid wine resort and spa', 'city29', '3', 18, 4.6), ('ven26', '02', 'argonaut hotel', 'city56', '3', 18, 4.9), ('ven27', '02', 'monte verde inn', 'city19', '3', 51, 4.8), ('ven28', '02', 'park winters', 'city73', '3', 41, 5.0), ('ven29', '02', 'club los meganos', 'city06', '3', 30, 4.9), ('ven30', '02', 'villa montalvo at the montalvo arts center', 'city65', '4', 26, 4.6), ('ven31', '02', 'deer park villa', 'city18', '4', 31, 4.6), ('ven32', '02', 'exploratorium', 'city56', '4', 12, 5.0), ('ven33', '02', 'the mountain winery', 'city65', '4', 10, 4.3), ('mus01', '03', 'lucky devils band', 'city51', '2', 287, 4.9), ('mus02', '03', 'elite entertainment', 'city51', '3', 150, 4.8), ('mus03', '03', 'vyby society', 'city51', '2', 127, 5.0), ('mus04', '03', 'ceremony djs', 'city51', '4', 106, 5.0), ('mus05', '03', 'verducci event productions', 'city05', '3', 106, 4.9), ('mus06', '03', 'mark addington events', 'city67', '2', 104, 5.0), ('mus07', '03', 'twin spin entertainment', 'city14', '3', 77, 5.0), ('mus08', '03', 'sf deejays', 'city14', '2', 70, 5.0), ('mus09', '03', 'quantum party productions', 'city51', '2', 70, 4.7), ('mus10', '03', 'the celebration dj', 'city51', '2', 68, 5.0), ('mus11', '03', 'bluedge productions', 'city51', '3', 63, 5.0), ('mus12', '03', 'sounds elevated', 'city51', '3', 62, 5.0), ('mus13', '03', 'retro jukebox band', 'city05', '2', 60, 5.0), ('mus14', '03', 'cover me badd', 'city26', '2', 60, 5.0), ('mus15', '03', 'on the beat', 'city51', '3', 50, 5.0), ('mus16', '03', 'fog city entertainment', 'city51', '2', 50, 4.9), ('mus17', '03', 'the cosmo alleycats -a dance band with a vintage twist!', 'city05', '2', 47, 5.0), ('mus18', '03', 'pop rocks', 'city51', '3', 47, 5.0), ('mus19', '03', 'notorious', 'city51', '2', 45, 5.0), ('mus20', '03', 'dj jeremy productions', 'city51', '3', 45, 4.9), ('mus21', '03', 'hella fitzgerald', 'city51', '3', 41, 4.9), ('mus22', '03', 'bay area beats dj', 'city51', '3', 41, 4.9), ('mus23', '03', 'alan waltz entertainment', 'city51', '3', 36, 4.9), ('mus24', '03', 'pantera event productions', 'city51', '2', 35, 5.0), ('mus25', '03', 'mercy and the heartbeats', 'city51', '2', 33, 5.0), ('mus26', '03', 'cartwrights dj services', 'city51', '2', 33, 5.0), ('mus27', '03', 'boutique djs', 'city51', '3', 33, 4.8), ('mus28', '03', 'wonder bread 5', 'city51', '3', 32, 5.0), ('mus29', '03', 'honey of the heart and soul graffiti entertainment', 'city51', '2', 32, 5.0), ('mus30', '03', 'dream entertainment', 'city51', '2', 32, 5.0), ('mus31', '03', 'ivy hill entertainment', 'city41', '3', 28, 4.8), ('mus32', '03', 'w square event production', 'city51', '4', 28, 5.0), ('mus33', '03', 'los gatos dj cmpany', 'city28', '4', 28, 5.0), ('mus34', '03', 'the cheeseballs', 'city51', '3', 27, 5.0), ('mus35', '03', 'audio ecstasy event sound and lighting', 'city51', '2', 26, 5.0), ('mus36', '03', 'bell and co', 'city51', '3', 25, 5.0), ('mus37', '03', 'classic vybe', 'city51', '3', 23, 5.0), ('mus38', '03', 'rdm entertainment', 'city41', '2', 21, 4.5), ('mus39', '03', 'hip entertainment and events', 'city51', '3', 19, 5.0), ('mus40', '03', 'dj big cali llc', 'city41', '3', 19, 5.0), ('mus41', '03', 'radio gatsby', 'city51', '3', 18, 5.0), ('mus42', '03', 'lori carsillo jazz vocalist and ensemble', 'city51', '2', 18, 5.0), ('mus43', '03', 'mark welch entertainment', 'city30', '3', 18, 4.8), ('mus44', '03', 'the freshmakers band', 'city51', '2', 16, 5.0), ('mus45', '03', 'the klipptones', 'city51', '2', 16, 5.0), ('mus46', '03', 'spotlight occasions', 'city10', '1', 15, 4.9), ('mus47', '03', 'bay area goodtime wedding djs', 'city51', '2', 14, 4.9), ('mus48', '03', 'harpist krista strader', 'city51', '3', 13, 5.0), ('mus49', '03', 'synchonicity strings', 'city51', '3', 11, 4.7), ('mus50', '03', 'amethyst trio', 'city51', '2', 10, 5.0), ('jwl01', '04', 'derco fine jewelers', 'city56', '2', 1046, 4.8), ('jwl02', '04', 'brilliant earth', 'city56', '3', 1007, 4.2), ('jwl03', '04', 'geoffreys diamonds and goldsmith', 'city55', '2', 718, 4.9), ('jwl04', '04', 'shane co walnutcreek01', 'city72', '2', 520, 4.2), ('jwl05', '04', 'yadav diamonds and jewelry', 'city56', '2', 391, 4.8), ('jwl06', '04', 'la bijouterie', 'city56', '3', 312, 4.9), ('jwl07', '04', 'barons', 'city16', '3', 270, 4.8), ('jwl08', '04', 'appelblom jewelry', 'city59', '2', 259, 4.9), ('jwl09', '04', 'just bands', 'city56', '2', 210, 4.9), ('jwl10', '04', 'oaks jewelry', 'city05', '2', 208, 4.8), ('jwl11', '04', 'shane co cupertino01', 'city13', '2', 743, 4.3), ('jwl12', '04', 'shane co sanmateo01', 'city59', '2', 329, 4.3), ('jwl13', '04', 'design jewelers', 'city56', '2', 323, 5.0), ('jwl14', '04', 'heller jewelers', 'city62', '3', 240, 4.5), ('jwl15', '04', 'pavé fine jewelry', 'city45', '3', 190, 4.6), ('jwl16', '04', 'spitz jewelers', 'city45', '3', 166, 4.4), ('jwl17', '04', 'vardys jewelers', 'city13', '2', 430, 4.8), ('jwl18', '04', 'newark jewelry center', 'city41', '2', 356, 4.9), ('jwl19', '04', 'neil dahl jewelers of california', 'city34', '2', 288, 4.7), ('jwl20', '04', 'sausalito jewelers', 'city66', '3', 109, 5.0), ('jwl21', '04', 'willow glen diamond cmpany', 'city57', '2', 258, 4.8), ('jwl22', '04', 'joe escobar', 'city09', '3', 446, 4.7), ('jwl23', '04', 'jin wang', 'city56', '3', 277, 4.7), ('jwl24', '04', 'topper fines jewelry', 'city08', '3', 232, 4.7), ('jwl25', '04', 'the love of ganesha', 'city56', '1', 455, 4.9), ('jwl26', '04', 'master fix jewelers', 'city09', '1', 342, 4.8), ('jwl27', '04', 'general bead', 'city56', '1', 221, 3.9), ('jwl28', '04', 'louis vuitton bloomingdales santa clara', 'city57', '4', 677, 2.7), ('jwl29', '04', 'tiffany and co', 'city56', '4', 384, 3.5), ('jwl30', '04', 'chanel', 'city56', '4', 358, 3.5), ('pho01', '05', 'of his fold photography', 'city11', '2', 10, 5.0), ('pho02', '05', 'michaels wedding group', 'city13', '1', 16, 4.9), ('pho03', '05', 'kates captures photography', 'city14', '3', 65, 5.0), ('pho04', '05', 'photostm bay area photography', 'city20', '2', 12, 4.9), ('pho05', '05', 'julia rose photography', 'city24', '2', 14, 5.0), ('pho06', '05', 'megan trant photography', 'city29', '2', 13, 5.0), ('pho07', '05', 'adeline and grace photography', 'city40', '3', 53, 5.0), ('pho08', '05', 'kat ma photography', 'city45', '3', 112, 4.9), ('pho09', '05', 'joss li photo', 'city54', '2', 11, 5.0), ('pho10', '05', 'george street photo and video', 'city56', '1', 2610, 4.3), ('pho11', '05', 'duy ho photography', 'city56', '3', 75, 5.0), ('pho12', '05', 'home for brides wedding photography', 'city56', '1', 174, 5.0), ('pho13', '05', 'alexis exstrom photography', 'city56', '2', 19, 5.0), ('pho14', '05', 'anne-claire brun', 'city56', '2', 35, 5.0), ('pho15', '05', 'tyler vu photography', 'city57', '3', 33, 5.0), ('pho16', '05', 'imanstudiosnet', 'city57', '2', 10, 5.0), ('pho17', '05', 'tanya and victor', 'city61', '2', 13, 5.0), ('pho18', '05', 'martha hoang photography', 'city63', '3', 16, 5.0), ('pho19', '05', 'delumpa photography', 'city56', '3', 48, 5.0), ('pho20', '05', 'alice che photography', 'city69', '4', 16, 5.0),

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