%%capture
%pip install pandas matplotlib pymongo seaborn --no-cache-dir

About the Project

Overview

The project involves the statistical analysis of the official results of the Intra-School Council Elections using libraries like Pandas, Matplotlib & Seaborn along with SQL.

The student council elections are held every year to choose representatives for different leadership roles in the school, managed entirely by student-led tech teams.

Election Software

The (1)Election Software through which the elections were held is a fullstack application that I designed and developed, featuring a frontend built with HTML, CSS, and TypeScript, and a REST api by FastAPI (Python). Votes were securely stored in a MongoDB database.

Report Generation

This report is in fact a single Jupyter notebook exported via a custom script to HTML and styled via CSS, (and then printed). Source of which is available at (2)GitHub Repo.

References

(1) https://github.com/d1vij/electionsoftware

(2) https://github.com/d1vij/ip-proj

Libraries used

  1. Pymongo -> for querying vote documents from MongoDB server
  2. sqlite3 -> for querying local sql database
  3. pandas -> Fora data manipulation and analysis
  4. seaborn -> Graphing
  5. matplotlib -> Graphing
import pymongo
import sqlite3
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt

Defining constants

_SPACE = " "
_UNDERSCORE = "_"
CONNECTION_URL = "mongodb+srv://vermadivij:elections@cluster1.kicphp2.mongodb.net/?retryWrites=true&w=majority&appName=cluster1"
DATABASE_NAME = "votes"
CLASSES = '10A 10B 10C 10D 10E 10F 10G 10H 10I 10J 11A 11B 11C 11D 11E 12A 12B 12C 12D 9A 9B 9C 9D 9E 9F 9G 9H 9I 9J absentees candidates'.split(_SPACE)

sns.set_theme()
def replace_spaces(string: str, replace_with=_UNDERSCORE):
    return string.replace(_SPACE, replace_with)
from typing import TypedDict, List

T_Post_Name = str
T_Candidate_Name = str

class T_Vote(TypedDict):
    """indivisual ballot/vote choice"""
    name: str
    post: str


class T_Vote_Document(TypedDict):
    """this is how data is stored on server"""
    token: str
    vote_data: List[T_Vote]


class T_Class_Documents(TypedDict):
    
    name: str
    votes: List[T_Vote]

class T_Classwise_Postwise_Totals(TypedDict):
    name: str
    votes: dict[T_Post_Name, dict[T_Candidate_Name, int]]

Structure of Data

The Votes were stored indivisually in a MongoDB server as in the following structure

        
//example single vote document
{
    "_id": {
        "$oid": "68a1819ceff178ec25b66fbb"            // internal mongodb document id
    },
    "token": "b489737f-7997-430c-950f-b8c1b22f68c3",  // A unique uuid4 based token identifing the vote session
    "client": "29",                                   // Computer on which the vote was done
    "vote_data": [                                    // candidates voted by the voter
        {
            "name": "Abhichandra Charke", 
            "post": "Captain Boy"
        },
        {
            "name": "Gauravi Zade",
            "post": "Captain Girl"
        },
        {
            "name": "Kausar Chandra",
            "post": "Vice Captain Boy"
        },
        {
            "name": "Ketaki Phalle",
            "post": "Vice Captain Girl"
        }
    ]
}

get_classwise_documents -> The function returns array of T_Class_Documents objects

In which each object contains properties

  1. name -> The name of class
  2. votes -> Array of T_Vote

T_Vote contains two properties

  1. name -> Name of candidate voted
  2. post-> Post the candidate is voted for

def get_classwise_documents() -> List[T_Class_Documents]:
    # fetches class-wise vote documents from mongodb cluster and returns dictionary with values as array of vote documents
    conn = pymongo.MongoClient(CONNECTION_URL)

    database = conn.get_database(DATABASE_NAME)

    all_documents: list[T_Class_Documents] = []

    vote_document: T_Vote_Document
    for class_name in CLASSES:

        class_documents: List[T_Vote] = []
        collection = database.get_collection(class_name)

        for vote_document in collection.find({}):
            class_documents.append(vote_document["vote_data"])  # type: ignore

        all_documents.append({"name": class_name, "votes": class_documents})

    return all_documents

Compiling classwise data into dataframes

calculate_total_votes_of_class → Function to compute total votes obtained by each candidate in a single class.

Working:

  1. Initializes a compiled_votes votes for each candidate under every post.

  2. Iterates through votes in a class document.

  3. Increments the candidate's count under the respective post.

  4. Returns an class name and compiled vote data.

create_dataframes → Function to transform compiled vote data into Pandas DataFrames for analysis.

Steps:

  1. Creates an empty DataFrame for each post, with CLASSES as rows and candidates as columns.

  2. Fills each DataFrame with vote counts from compiled_votes.

  3. Returns a dictionary of DataFrames, one per post, for further statistical analysis and visualization.

# Post-wise canddiate names
candidate_data = {
    "Captain Boy": [
        "Aadityaraje Desai",
        "Abhichandra Charke",
        "Praneel Deshmukh",
        "Rachit Srivastava",
    ],
    "Captain Girl": [
        "Tvisha Shah",
        "Gauravi Zade",
        "Kirthika Jayachander",
        "Naisha Rastogi",
    ],
    "Vice Captain Boy": [
        "Kausar Chandra",
        "Sagnik Ghosh",
        "Avaneesh Mahalle",
        "Krishna Yadav",
        "Viren Jadhav",
    ],
    "Vice Captain Girl": [
        "Ketaki Phalle",
        "Trisha Kandpal",
        "Riya Shirode",
        "Kavya Mehta",
        "Sumedha Vaidya",
    ],
}


# empty dictionary to store totaled votes data
empty_votes_dict = {
    post_name: {
        class_name: {name: 0 for name in candidate_data[post_name]}
        for class_name in CLASSES
    }
    for post_name in candidate_data.keys()
}


def calculate_total_votes_of_class(
    class_documents: T_Class_Documents,
) -> T_Classwise_Postwise_Totals:

    compiled_votes = {
        post_name: {candidate_name: 0 for candidate_name in candidate_data[post_name]}
        for post_name in candidate_data.keys()
    }

    vote_sesh: List[T_Vote]

    for vote_sesh in class_documents["votes"]: #type: ignore
        for vote_document in vote_sesh:
            post = vote_document["post"]
            name = vote_document["name"]
            compiled_votes[post][name] += 1

    return {"name": class_documents["name"], "votes": compiled_votes}


def create_dataframes(compiled_votes: list[T_Classwise_Postwise_Totals]):

    # creating empty dataframes
    dataframes = {
        post_name: pd.DataFrame(0, CLASSES, candidates)
        for post_name, candidates in candidate_data.items()
    }


    for _class in compiled_votes:
        class_name = _class["name"]

        for post_name, votes in _class["votes"].items():
            dataframes[post_name].loc[class_name] = votes # type: ignore
    return dataframes

The query() function is a wrapper around SQLite queries, supporting both data retrieval and data modification. 

from sqlite3 import OperationalError
from typing import Union, Literal

# typings
REPR_UNION = Union[Literal["string"], Literal["list"], None]
QUERY_UNION = Union[None, list[tuple[str]], str, list[str]]

def query(
    query: str,
    *,
    is_updation=False,  # is the current query contains some kind of updation ?? Doesnt return anything if true
    return_repr: REPR_UNION = None,  # Returns output as string or list of lines if passed any of the value. Prints the result if None.
    return_rows=False,  # Return rows as lists of tuples ??
    table_heading: str | None = None,  # Title printed before printing output
) -> QUERY_UNION:
    conn = sqlite3.connect(DATABASE_NAME+".db")
    cursor = conn.cursor()    
    try:
        results = cursor.execute(query)
    except OperationalError as err:
        print(f"Error in querying -> {query}")
        print("** Row / Column names with spaces should be enlcosed within quotes **")
        print(err)
        cursor.close()
        conn.close()
        return
        
    if is_updation:
        return
    if return_rows:
        return results.fetchall()
    else:
        lines: list[str] = []
        # printing table header if provided
        if table_heading is not None:
            print(table_heading)

        # printing column names
        lines.append(_SPACE.join(desc[0] for desc in results.description))

        # for most part left padding works fine
        label_lengths = [len(desc[0]) for desc in results.description]

        for row in results.fetchall():
            line = []
            for idx, col in enumerate(row):

                # left justifing current column value based on length fo current column's label
                line.append(str(col).ljust(label_lengths[idx] + 1))
            lines.append("".join(line))

        if return_repr == "string":
            return "\n".join(lines)
        elif return_repr == "list":
            return lines
        else:
            print(*lines, sep="\n")

    cursor.close()
    conn.close()

Creating post dataframes and saving them to SQLite database

class_wise_documents = get_classwise_documents()
result_dataframes = create_dataframes(
    list(map(calculate_total_votes_of_class, class_wise_documents))
)

print("Found Posts", *result_dataframes.keys(), sep="\n")

conn = sqlite3.connect(DATABASE_NAME + ".db")
cursor = conn.cursor()
for name, post_df in result_dataframes.items():
    name = name.replace(_SPACE, _UNDERSCORE)
    post_df.columns = [name.replace(_SPACE, _UNDERSCORE) for name in post_df.columns]
    post_df.to_sql(name, conn, if_exists="replace", index_label="Class")
    conn.commit()
conn.close()
Found Posts
Captain Boy
Captain Girl
Vice Captain Boy
Vice Captain Girl

# captian boy dataframe
cb = result_dataframes["Captain Boy"]
# captian girl dataframe
cg = result_dataframes["Captain Girl"]
# vice captian boy dataframe
vcb = result_dataframes["Vice Captain Boy"]
# vice captian girl dataframe
vcg = result_dataframes["Vice Captain Girl"]
print(f"Total voters : {cb.sum().sum()}")
print(f"Total classes: {len(CLASSES)}")
Total voters : 385
Total classes: 31

Statistical analysis

Total votes across all classes

Total votes recieved by any Candidate

Working:

  1. Defines positions for mapping each post to a subplot.

  2. Iterating over post dataframes in result_dataframes.

  3. Running SQL query to sum votes of in the table of that post via query().

  4. Plotting a Seaborn bar chart of the total votes (post_df.sum()) in its respective subplot.

fig, axes = plt.subplots(2,2, figsize=(15,10))
positions = [(0,0), (0,1), (1,0), (1,1)]

for idx, (post_name, post_df) in enumerate(result_dataframes.items()):
    query(
        f"""
        select {', '.join([f"sum({name}) as {name}" for name in post_df.columns])}
        from {replace_spaces(post_name)}
        """,
        table_heading="Total Votes for - " + post_name
    )
    print()
    sns.barplot(post_df.sum(), ax=axes[positions[idx]]) #type: ignore
    axes[positions[idx]].set_title(post_name)
plt.tight_layout()
plt.show()
print()

Total Votes for - Captain Boy
Aadityaraje_Desai Abhichandra_Charke Praneel_Deshmukh Rachit_Srivastava
148               70                 151              16                

Total Votes for - Captain Girl
Tvisha_Shah Gauravi_Zade Kirthika_Jayachander Naisha_Rastogi
41          252          20                   72             

Total Votes for - Vice Captain Boy
Kausar_Chandra Sagnik_Ghosh Avaneesh_Mahalle Krishna_Yadav Viren_Jadhav
82             36           136              77            54           

Total Votes for - Vice Captain Girl
Ketaki_Phalle Trisha_Kandpal Riya_Shirode Kavya_Mehta Sumedha_Vaidya
175           80             11           84          35             


Candidate popularity trends



    
Comparing candidate performances across classes

    
Following steps taken for each post's dataframe

    

        
Iterating over post dataframes and extracting all the rows belonging to a particular 'standard' from the post's dataframe by using Regular Expressions

        
Dividing plot into 4 subplots for each class (9, 10, 11, 12)

        
Plotting the section-wise votes recieved by a candidate

    




# candidate popularity trends - comparing candidate performances across classes

from matplotlib.ticker import MultipleLocator


def plot_popularity_trends(post_name: str, post_df: pd.DataFrame):

    # extracting rows belonging to a particular class from the post's dataframe using regular expressions
    class_wise_dataframes = [
        post_df[post_df.index.str.contains(_re)]
        for _re in [r"9\w", r"10\w", r"11\w", r"12\w"]  # <--- regex btw
    ]

    # dividing the plot into 4 subplots
    fig, axes = plt.subplots(2, 2, figsize=(15, 7))

    subplot_positions = [
        (0, 0),
        (0, 1),
        (1, 0),
        (1, 1),
    ]  # since there are only 4 classes / subplots
    linestyles = [":", "-", "--", "-.", "solid"]

    for idx in range(4):
        pos = subplot_positions[idx]
        class_df = class_wise_dataframes[idx]
        sections = class_df.index

        for idx, (candidate_name, candidate_series) in enumerate(class_df.items()):
            # plotting a subplot for each class
            axes[pos].plot(
                sections,
                candidate_series,
                label=candidate_name.replace(_UNDERSCORE, _SPACE), #type: ignore
                linestyle=linestyles[idx],
            ) 

        axes[pos].set_xlabel("class")
        axes[pos].set_ylabel("Votes")

        # axes[pos].set_ylim(0, post_df.max().max() + 1)

        # values on y-axis would have a difference of 2
        axes[pos].yaxis.set_major_locator(MultipleLocator(2))

    fig.suptitle(post_name, fontsize=32)

    # setting a common legend for the whole plot
    handles, labels = axes[0, 0].get_legend_handles_labels()
    fig.legend(handles, labels, loc="upper right", ncols=2, fontsize=15)

    plt.show()


plot_popularity_trends("Captain Boy", cb)
plot_popularity_trends("Captain Girl", cg)
plot_popularity_trends("Vice Captain Boy", vcb)
plot_popularity_trends("Vice Captain Girl", vcg)
Plotting the share in percent of classes in which a candidate has majority



    
Working:

    
  1. Dividing the plot into four subplots
  2. Extracting the count of classes in which a particular candidate has the maximum votes amongst all other candidates of same post
  3. Dividing the series obtained in previous step witht the total number of votes to get the percent share series
  4. Plotting the percent share series


total_classes = len(cb.index)

fig, axes = plt.subplots(2, 2, figsize=(10, 7), constrained_layout=True)
fig.suptitle("Share (percent) of Classes in which a Candidate has a majority    ", fontsize=20)

subplot_positions = [
        (0, 0),
        (0, 1),
        (1, 0),
        (1, 1),
    ]

colors = plt.cm.copper_r(np.linspace(0,0.50,5)) # type: ignore


for idx, (post_name, post_df) in enumerate(result_dataframes.items()):
    pos = subplot_positions[idx]

    # Column wise maximum will give the winning candidate of that class
    classes_won_by_candidate_series = post_df.idxmax(1)
    count_series = (classes_won_by_candidate_series
                        .groupby(classes_won_by_candidate_series)
                        .count()
                        .sort_values(ascending=False)
                        )
    percents_series = count_series / total_classes
    
    max_val = percents_series.max()

    axes[pos].set_title(post_name)
    axes[pos].pie(
        percents_series,
        labels=percents_series.index.map(
            lambda name: name.replace(_UNDERSCORE, _SPACE)
        ),
        autopct="%1.1f%%",
        startangle=180,
        colors=colors,
    )
    axes[pos].set(aspect='equal')


plt.show()

    
Furthermore we can notice despite being the second leading candidate (Aadityaraje Desai), they have the almost double the class-wise majority share than the leading candidate (Praneel Deshmukh) for the post of School Captain

    
The absence of fifth candidate (Riya Shirode) in the fourth pie shows that they are not the majority in any class amongst all other candidates of the same post


Candidate Co-Voting Patterns




    
Analyzes whether voters who supported one candidate also tended to support another.

    
Working:





    
    
        
  1. Builds a vote matrix recording which candidates were chosen in each voting session.
    // Example
    
Captain Boy  Captain Girl  Vice Captain Boy Vice Captain Girl
25    Praneel Deshmukh  Gauravi Zade      Viren Jadhav       Kavya Mehta
26   Aadityaraje Desai  Gauravi Zade  Avaneesh Mahalle       Kavya Mehta
27  Abhichandra Charke  Gauravi Zade  Avaneesh Mahalle     Ketaki Phalle
28    Praneel Deshmukh  Gauravi Zade      Sagnik Ghosh    Sumedha Vaidya
29  Abhichandra Charke  Gauravi Zade      Sagnik Ghosh    Sumedha Vaidya
30  Abhichandra Charke  Gauravi Zade  Avaneesh Mahalle     Ketaki Phalle
31    Praneel Deshmukh  Gauravi Zade     Krishna Yadav       Kavya Mehta
32    Praneel Deshmukh  Gauravi Zade  Avaneesh Mahalle     Ketaki Phalle
33  Abhichandra Charke  Gauravi Zade      Viren Jadhav     Ketaki Phalle
34  Abhichandra Charke  Gauravi Zade    Kausar Chandra     Ketaki Phalle
    2. 

  2. Constructs a co-occurrence matrix showing how often Candidate B was voted when Candidate A was voted.
    // Example
    
                    Gauravi Zade  Kirthika Jayachander  Naisha Rastogi
Avaneesh Mahalle            94                     4              26   
Krishna Yadav               54                     3              14   
Viren Jadhav                33                     2              11   
Ketaki Phalle              116                     5              38   
Trisha Kandpal              52                     5              15   
Riya Shirode                 5                     3               0   
Kavya Mehta                 52                     3              18   
Sumedha Vaidya              27                     4               1   

  3. Normalizes it into a probability matrix to estimate the likelihood of co-support between candidates. Each row of the co-occurrence matrix is divided by the total votes in that row. This converts raw counts into conditional probabilities, i.e., the chance of Candidate B being voted given that Candidate A was voted.
  4. Visualizes both matrices using heatmaps — one for raw counts, the other for probabilities.


# constructing votes matrix
# vote matrix contains which candidate was voted for which post in any particular voting session
vote_matrix = pd.DataFrame(columns=list(candidate_data.keys()))
for _class in class_wise_documents:
    for session_votes in _class["votes"]:

        idx = len(vote_matrix)
        vote_dict = {}
        for vote in session_votes:
            vote_dict[vote["post"]] = vote["name"]  # type:ignore

        vote_matrix.loc[idx] = vote_dict

all_candidates = []
for _, candidates in candidate_data.items():
    all_candidates.extend(candidates)

# co-occurance matrix is the matrix showing how many times candidate B was voted when candidate A was voted
# co-occurance matrix would be N * N where N are the total number of candidates across all posts (18 * 18 for this case)
co_occurance_matrix = pd.DataFrame(0, index=all_candidates, columns=all_candidates)

# updating co-occurance matrix
for idx, session in vote_matrix.iterrows():
    for name_A in session.values:
        for name_B in session.values:
            if name_A != name_B:
                co_occurance_matrix.loc[name_A, name_B] += 1

# creating conditional probability matrix
# conditional probabilty matrix is created by normalizing columns of co-occurance matrix
# normalizing means dividing each row of co-occurance matrix by the total votes in that row
# the matrix gives the probabilty of person b (x axis) being voted when person A (y axis) was voted
probability_matrix = co_occurance_matrix.div(co_occurance_matrix.sum(axis=1), axis=0)

# ---- first plot ----
fig1, ax1 = plt.subplots(figsize=(12, 8))
ax1.set_title(
    "Co-occurance plot - Number of times person A got voted when person B was voted",
    size=16,
)
sns.heatmap(
    co_occurance_matrix,
    cmap="viridis",
    vmin=0,
    annot=True,
    ax=ax1,
    fmt=".0f",
    cbar_kws={"label": "Count"},
)
ax1.set_ylabel("Person A", size=12)
ax1.set_xlabel("Person B", size=12)
ax1.set_xticklabels(ax1.get_xticklabels(), rotation=90)
ax1.set_yticklabels(ax1.get_yticklabels(), rotation=0)
plt.show()

# ---- second plot ----
fig2, ax2 = plt.subplots(figsize=(12, 8))
ax2.set_title(
    "Probability plot - Probabilty (in percent) of person B getting voted when person A was voted",
    size=16,
)
sns.heatmap(
    probability_matrix * 100,
    cmap="viridis",
    vmin=0,
    annot=True,
    ax=ax2,
    fmt=".1f",
    cbar_kws={"label": "Percent"},
)
ax2.set_ylabel("Person A", size=12)
ax2.set_xlabel("Person B", size=12)
ax2.set_xticklabels(ax2.get_xticklabels(), rotation=90)
ax2.set_yticklabels(ax2.get_yticklabels(), rotation=0)
plt.show()

# the percents here for a column dont add up to 100 cuz they are mutually exclusive events

Strongest & Weakest Allies




    
Identifies which candidates tend to appear most often (or least often) alongside another candidate in votes.






    
Working:

    
  1. For each candidate, extracts their row from the probability matrix (probability of other candidates being voted when this candidate is chosen).
  2. Removes same-post candidates to avoid trivial overlaps (since voters pick only one per post).
  3. Finds the Strongest Ally → candidate with the highest co-vote probability.
  4. Finds the Weakest Ally → candidate with the lowest co-vote probability.
  5. Combines results into a summary table, showing strongest and weakest allies for each candidate.


# Use row indexes for all comparisions

strongest_ally_series = pd.Series(name="Strongest Ally")
weakest_ally_series = pd.Series(name="Weakest Ally")

for post_name, same_post_candidates in candidate_data.items():
    for name in same_post_candidates:
        # extracting the row which gives co-occurance probabilty for a candidate
        cps = probability_matrix.loc[name]

        # removing values of all the candidates in the same post
        candidate_probability_series = cps[~cps.index.isin(same_post_candidates)]

        strongest_ally_series[name] = candidate_probability_series.idxmax()
        weakest_ally_series[name] = candidate_probability_series.idxmin()

# concat based on similar rows
summary = pd.concat([strongest_ally_series, weakest_ally_series], axis=1)
print(summary.sort_values(by=list(summary.columns)))

# strongest ally is the candidate who is most likely to be voted when a candidate is voted
# weakest ally is the candidate who is least likely to be voted when a candidate is voted
                         Strongest Ally          Weakest Ally
Kirthika Jayachander  Aadityaraje Desai    Abhichandra Charke
Ketaki Phalle              Gauravi Zade  Kirthika Jayachander
Rachit Srivastava          Gauravi Zade        Naisha Rastogi
Sumedha Vaidya             Gauravi Zade        Naisha Rastogi
Sagnik Ghosh               Gauravi Zade     Rachit Srivastava
Viren Jadhav               Gauravi Zade     Rachit Srivastava
Trisha Kandpal             Gauravi Zade     Rachit Srivastava
Kavya Mehta                Gauravi Zade     Rachit Srivastava
Aadityaraje Desai          Gauravi Zade          Riya Shirode
Abhichandra Charke         Gauravi Zade          Riya Shirode
Praneel Deshmukh           Gauravi Zade          Riya Shirode
Kausar Chandra             Gauravi Zade          Riya Shirode
Avaneesh Mahalle           Gauravi Zade          Riya Shirode
Krishna Yadav              Gauravi Zade          Riya Shirode
Gauravi Zade              Ketaki Phalle          Riya Shirode
Naisha Rastogi            Ketaki Phalle          Riya Shirode
Riya Shirode           Praneel Deshmukh    Abhichandra Charke
Tvisha Shah            Praneel Deshmukh     Rachit Srivastava

# mean co-support - mean of all conditional probabilties across all candidates
probability_matrix.mean() * 100
# exporting votes to csv
vote_matrix.to_csv("votes.csv")
Raw csv votes
index,Captain Boy,Captain Girl,Vice Captain Boy,Vice Captain Girl
0,Aadityaraje Desai,Tvisha Shah,Kausar Chandra,Ketaki Phalle
1,Abhichandra Charke,Tvisha Shah,Sagnik Ghosh,Ketaki Phalle
2,Aadityaraje Desai,Tvisha Shah,Avaneesh Mahalle,Trisha Kandpal
3,Aadityaraje Desai,Gauravi Zade,Kausar Chandra,Riya Shirode
4,Abhichandra Charke,Gauravi Zade,Krishna Yadav,Ketaki Phalle
5,Abhichandra Charke,Gauravi Zade,Kausar Chandra,Kavya Mehta
6,Praneel Deshmukh,Gauravi Zade,Krishna Yadav,Ketaki Phalle
7,Aadityaraje Desai,Kirthika Jayachander,Avaneesh Mahalle,Trisha Kandpal
8,Praneel Deshmukh,Gauravi Zade,Avaneesh Mahalle,Trisha Kandpal
9,Aadityaraje Desai,Naisha Rastogi,Krishna Yadav,Trisha Kandpal
10,Abhichandra Charke,Gauravi Zade,Kausar Chandra,Trisha Kandpal
11,Abhichandra Charke,Gauravi Zade,Avaneesh Mahalle,Ketaki Phalle
12,Praneel Deshmukh,Naisha Rastogi,Avaneesh Mahalle,Ketaki Phalle
13,Praneel Deshmukh,Gauravi Zade,Avaneesh Mahalle,Sumedha Vaidya
14,Praneel Deshmukh,Gauravi Zade,Avaneesh Mahalle,Sumedha Vaidya
15,Praneel Deshmukh,Gauravi Zade,Sagnik Ghosh,Sumedha Vaidya
16,Aadityaraje Desai,Gauravi Zade,Krishna Yadav,Sumedha Vaidya
17,Praneel Deshmukh,Naisha Rastogi,Kausar Chandra,Ketaki Phalle
18,Aadityaraje Desai,Gauravi Zade,Viren Jadhav,Sumedha Vaidya
19,Praneel Deshmukh,Tvisha Shah,Kausar Chandra,Trisha Kandpal
20,Abhichandra Charke,Tvisha Shah,Sagnik Ghosh,Ketaki Phalle
21,Abhichandra Charke,Naisha Rastogi,Kausar Chandra,Kavya Mehta
22,Aadityaraje Desai,Gauravi Zade,Krishna Yadav,Kavya Mehta
23,Abhichandra Charke,Gauravi Zade,Viren Jadhav,Kavya Mehta
24,Aadityaraje Desai,Gauravi Zade,Viren Jadhav,Kavya Mehta
25,Praneel Deshmukh,Gauravi Zade,Viren Jadhav,Kavya Mehta
26,Aadityaraje Desai,Gauravi Zade,Avaneesh Mahalle,Kavya Mehta
27,Abhichandra Charke,Gauravi Zade,Avaneesh Mahalle,Ketaki Phalle
28,Praneel Deshmukh,Gauravi Zade,Sagnik Ghosh,Sumedha Vaidya
29,Abhichandra Charke,Gauravi Zade,Sagnik Ghosh,Sumedha Vaidya
30,Abhichandra Charke,Gauravi Zade,Avaneesh Mahalle,Ketaki Phalle
31,Praneel Deshmukh,Gauravi Zade,Krishna Yadav,Kavya Mehta
32,Praneel Deshmukh,Gauravi Zade,Avaneesh Mahalle,Ketaki Phalle
33,Abhichandra Charke,Gauravi Zade,Viren Jadhav,Ketaki Phalle
34,Abhichandra Charke,Gauravi Zade,Kausar Chandra,Ketaki Phalle
35,Aadityaraje Desai,Gauravi Zade,Avaneesh Mahalle,Kavya Mehta
36,Praneel Deshmukh,Tvisha Shah,Viren Jadhav,Kavya Mehta
37,Praneel Deshmukh,Naisha Rastogi,Avaneesh Mahalle,Ketaki Phalle
38,Abhichandra Charke,Gauravi Zade,Sagnik Ghosh,Ketaki Phalle
39,Praneel Deshmukh,Gauravi Zade,Sagnik Ghosh,Ketaki Phalle
40,Abhichandra Charke,Gauravi Zade,Krishna Yadav,Kavya Mehta
41,Praneel Deshmukh,Gauravi Zade,Avaneesh Mahalle,Ketaki Phalle
42,Praneel Deshmukh,Gauravi Zade,Avaneesh Mahalle,Kavya Mehta
43,Praneel Deshmukh,Naisha Rastogi,Kausar Chandra,Trisha Kandpal
44,Praneel Deshmukh,Naisha Rastogi,Kausar Chandra,Trisha Kandpal
45,Praneel Deshmukh,Gauravi Zade,Kausar Chandra,Kavya Mehta
46,Praneel Deshmukh,Gauravi Zade,Avaneesh Mahalle,Ketaki Phalle
47,Aadityaraje Desai,Gauravi Zade,Krishna Yadav,Ketaki Phalle
48,Praneel Deshmukh,Gauravi Zade,Kausar Chandra,Ketaki Phalle
49,Abhichandra Charke,Gauravi Zade,Avaneesh Mahalle,Ketaki Phalle
50,Praneel Deshmukh,Naisha Rastogi,Krishna Yadav,Ketaki Phalle
51,Praneel Deshmukh,Naisha Rastogi,Kausar Chandra,Ketaki Phalle
52,Praneel Deshmukh,Naisha Rastogi,Avaneesh Mahalle,Kavya Mehta
53,Praneel Deshmukh,Naisha Rastogi,Avaneesh Mahalle,Kavya Mehta
54,Praneel Deshmukh,Gauravi Zade,Avaneesh Mahalle,Trisha Kandpal
55,Praneel Deshmukh,Naisha Rastogi,Avaneesh Mahalle,Ketaki Phalle
56,Praneel Deshmukh,Gauravi Zade,Kausar Chandra,Trisha Kandpal
57,Praneel Deshmukh,Gauravi Zade,Kausar Chandra,Ketaki Phalle
58,Aadityaraje Desai,Gauravi Zade,Avaneesh Mahalle,Ketaki Phalle
59,Aadityaraje Desai,Gauravi Zade,Sagnik Ghosh,Trisha Kandpal
60,Aadityaraje Desai,Kirthika Jayachander,Viren Jadhav,Ketaki Phalle
61,Aadityaraje Desai,Gauravi Zade,Avaneesh Mahalle,Trisha Kandpal
62,Aadityaraje Desai,Gauravi Zade,Viren Jadhav,Ketaki Phalle
63,Aadityaraje Desai,Gauravi Zade,Sagnik Ghosh,Trisha Kandpal
64,Praneel Deshmukh,Naisha Rastogi,Avaneesh Mahalle,Ketaki Phalle
65,Aadityaraje Desai,Naisha Rastogi,Sagnik Ghosh,Sumedha Vaidya
66,Rachit Srivastava,Kirthika Jayachander,Sagnik Ghosh,Riya Shirode
67,Praneel Deshmukh,Naisha Rastogi,Krishna Yadav,Ketaki Phalle
68,Aadityaraje Desai,Naisha Rastogi,Krishna Yadav,Ketaki Phalle
69,Aadityaraje Desai,Naisha Rastogi,Viren Jadhav,Ketaki Phalle
70,Aadityaraje Desai,Gauravi Zade,Kausar Chandra,Ketaki Phalle
71,Praneel Deshmukh,Gauravi Zade,Avaneesh Mahalle,Ketaki Phalle
72,Rachit Srivastava,Gauravi Zade,Viren Jadhav,Ketaki Phalle
73,Praneel Deshmukh,Gauravi Zade,Viren Jadhav,Ketaki Phalle
74,Abhichandra Charke,Gauravi Zade,Avaneesh Mahalle,Ketaki Phalle
75,Praneel Deshmukh,Gauravi Zade,Avaneesh Mahalle,Ketaki Phalle
76,Aadityaraje Desai,Gauravi Zade,Avaneesh Mahalle,Ketaki Phalle
77,Aadityaraje Desai,Gauravi Zade,Avaneesh Mahalle,Ketaki Phalle
78,Praneel Deshmukh,Gauravi Zade,Krishna Yadav,Ketaki Phalle
79,Aadityaraje Desai,Gauravi Zade,Avaneesh Mahalle,Ketaki Phalle
80,Aadityaraje Desai,Gauravi Zade,Avaneesh Mahalle,Ketaki Phalle
81,Abhichandra Charke,Gauravi Zade,Avaneesh Mahalle,Ketaki Phalle
82,Aadityaraje Desai,Gauravi Zade,Avaneesh Mahalle,Trisha Kandpal
83,Praneel Deshmukh,Naisha Rastogi,Avaneesh Mahalle,Trisha Kandpal
84,Aadityaraje Desai,Gauravi Zade,Avaneesh Mahalle,Ketaki Phalle
85,Aadityaraje Desai,Gauravi Zade,Krishna Yadav,Ketaki Phalle
86,Aadityaraje Desai,Gauravi Zade,Avaneesh Mahalle,Ketaki Phalle
87,Praneel Deshmukh,Tvisha Shah,Avaneesh Mahalle,Trisha Kandpal
88,Praneel Deshmukh,Gauravi Zade,Avaneesh Mahalle,Ketaki Phalle
89,Praneel Deshmukh,Gauravi Zade,Avaneesh Mahalle,Trisha Kandpal
90,Praneel Deshmukh,Gauravi Zade,Avaneesh Mahalle,Ketaki Phalle
91,Aadityaraje Desai,Gauravi Zade,Krishna Yadav,Sumedha Vaidya
92,Praneel Deshmukh,Tvisha Shah,Sagnik Ghosh,Trisha Kandpal
93,Praneel Deshmukh,Gauravi Zade,Sagnik Ghosh,Kavya Mehta
94,Praneel Deshmukh,Gauravi Zade,Avaneesh Mahalle,Trisha Kandpal
95,Aadityaraje Desai,Kirthika Jayachander,Avaneesh Mahalle,Sumedha Vaidya
96,Aadityaraje Desai,Gauravi Zade,Kausar Chandra,Trisha Kandpal
97,Abhichandra Charke,Gauravi Zade,Avaneesh Mahalle,Ketaki Phalle
98,Praneel Deshmukh,Gauravi Zade,Kausar Chandra,Trisha Kandpal
99,Abhichandra Charke,Gauravi Zade,Avaneesh Mahalle,Sumedha Vaidya
100,Abhichandra Charke,Gauravi Zade,Avaneesh Mahalle,Sumedha Vaidya
101,Abhichandra Charke,Gauravi Zade,Avaneesh Mahalle,Ketaki Phalle
102,Praneel Deshmukh,Gauravi Zade,Avaneesh Mahalle,Trisha Kandpal
103,Praneel Deshmukh,Naisha Rastogi,Kausar Chandra,Trisha Kandpal
104,Praneel Deshmukh,Gauravi Zade,Sagnik Ghosh,Trisha Kandpal
105,Aadityaraje Desai,Gauravi Zade,Krishna Yadav,Sumedha Vaidya
106,Aadityaraje Desai,Gauravi Zade,Kausar Chandra,Ketaki Phalle
107,Aadityaraje Desai,Gauravi Zade,Kausar Chandra,Ketaki Phalle
108,Aadityaraje Desai,Kirthika Jayachander,Krishna Yadav,Trisha Kandpal
109,Aadityaraje Desai,Gauravi Zade,Avaneesh Mahalle,Ketaki Phalle
110,Praneel Deshmukh,Gauravi Zade,Avaneesh Mahalle,Ketaki Phalle
111,Praneel Deshmukh,Gauravi Zade,Avaneesh Mahalle,Trisha Kandpal
112,Praneel Deshmukh,Gauravi Zade,Sagnik Ghosh,Trisha Kandpal
113,Praneel Deshmukh,Naisha Rastogi,Krishna Yadav,Ketaki Phalle
114,Abhichandra Charke,Gauravi Zade,Kausar Chandra,Ketaki Phalle
115,Praneel Deshmukh,Gauravi Zade,Kausar Chandra,Ketaki Phalle
116,Praneel Deshmukh,Gauravi Zade,Kausar Chandra,Ketaki Phalle
117,Aadityaraje Desai,Gauravi Zade,Kausar Chandra,Ketaki Phalle
118,Praneel Deshmukh,Gauravi Zade,Avaneesh Mahalle,Ketaki Phalle
119,Praneel Deshmukh,Gauravi Zade,Avaneesh Mahalle,Trisha Kandpal
120,Praneel Deshmukh,Gauravi Zade,Avaneesh Mahalle,Ketaki Phalle
121,Praneel Deshmukh,Naisha Rastogi,Kausar Chandra,Ketaki Phalle
122,Abhichandra Charke,Gauravi Zade,Kausar Chandra,Ketaki Phalle
123,Praneel Deshmukh,Kirthika Jayachander,Sagnik Ghosh,Sumedha Vaidya
124,Aadityaraje Desai,Gauravi Zade,Sagnik Ghosh,Sumedha Vaidya
125,Aadityaraje Desai,Gauravi Zade,Sagnik Ghosh,Sumedha Vaidya
126,Praneel Deshmukh,Gauravi Zade,Sagnik Ghosh,Sumedha Vaidya
127,Aadityaraje Desai,Tvisha Shah,Sagnik Ghosh,Sumedha Vaidya
128,Aadityaraje Desai,Naisha Rastogi,Sagnik Ghosh,Ketaki Phalle
129,Rachit Srivastava,Naisha Rastogi,Kausar Chandra,Ketaki Phalle
130,Praneel Deshmukh,Gauravi Zade,Kausar Chandra,Ketaki Phalle
131,Aadityaraje Desai,Gauravi Zade,Viren Jadhav,Riya Shirode
132,Praneel Deshmukh,Naisha Rastogi,Viren Jadhav,Ketaki Phalle
133,Aadityaraje Desai,Naisha Rastogi,Viren Jadhav,Ketaki Phalle
134,Aadityaraje Desai,Tvisha Shah,Avaneesh Mahalle,Ketaki Phalle
135,Abhichandra Charke,Naisha Rastogi,Avaneesh Mahalle,Ketaki Phalle
136,Praneel Deshmukh,Tvisha Shah,Krishna Yadav,Kavya Mehta
137,Rachit Srivastava,Tvisha Shah,Avaneesh Mahalle,Ketaki Phalle
138,Aadityaraje Desai,Tvisha Shah,Avaneesh Mahalle,Ketaki Phalle
139,Praneel Deshmukh,Naisha Rastogi,Avaneesh Mahalle,Ketaki Phalle
140,Praneel Deshmukh,Gauravi Zade,Avaneesh Mahalle,Ketaki Phalle
141,Praneel Deshmukh,Naisha Rastogi,Avaneesh Mahalle,Ketaki Phalle
142,Aadityaraje Desai,Naisha Rastogi,Avaneesh Mahalle,Ketaki Phalle
143,Praneel Deshmukh,Naisha Rastogi,Avaneesh Mahalle,Ketaki Phalle
144,Praneel Deshmukh,Gauravi Zade,Avaneesh Mahalle,Ketaki Phalle
145,Aadityaraje Desai,Naisha Rastogi,Kausar Chandra,Trisha Kandpal
146,Abhichandra Charke,Naisha Rastogi,Kausar Chandra,Trisha Kandpal
147,Praneel Deshmukh,Kirthika Jayachander,Avaneesh Mahalle,Trisha Kandpal
148,Aadityaraje Desai,Gauravi Zade,Kausar Chandra,Sumedha Vaidya
149,Rachit Srivastava,Gauravi Zade,Krishna Yadav,Trisha Kandpal
150,Praneel Deshmukh,Naisha Rastogi,Krishna Yadav,Kavya Mehta
151,Abhichandra Charke,Gauravi Zade,Krishna Yadav,Kavya Mehta
152,Praneel Deshmukh,Tvisha Shah,Krishna Yadav,Kavya Mehta
153,Aadityaraje Desai,Tvisha Shah,Avaneesh Mahalle,Kavya Mehta
154,Aadityaraje Desai,Tvisha Shah,Avaneesh Mahalle,Kavya Mehta
155,Aadityaraje Desai,Tvisha Shah,Avaneesh Mahalle,Kavya Mehta
156,Abhichandra Charke,Kirthika Jayachander,Kausar Chandra,Ketaki Phalle
157,Praneel Deshmukh,Tvisha Shah,Kausar Chandra,Kavya Mehta
158,Abhichandra Charke,Gauravi Zade,Avaneesh Mahalle,Ketaki Phalle
159,Abhichandra Charke,Gauravi Zade,Avaneesh Mahalle,Ketaki Phalle
160,Abhichandra Charke,Gauravi Zade,Avaneesh Mahalle,Kavya Mehta
161,Aadityaraje Desai,Gauravi Zade,Avaneesh Mahalle,Ketaki Phalle
162,Aadityaraje Desai,Kirthika Jayachander,Kausar Chandra,Kavya Mehta
163,Aadityaraje Desai,Kirthika Jayachander,Kausar Chandra,Ketaki Phalle
164,Praneel Deshmukh,Kirthika Jayachander,Krishna Yadav,Riya Shirode
165,Aadityaraje Desai,Kirthika Jayachander,Sagnik Ghosh,Sumedha Vaidya
166,Abhichandra Charke,Gauravi Zade,Avaneesh Mahalle,Ketaki Phalle
167,Aadityaraje Desai,Gauravi Zade,Avaneesh Mahalle,Ketaki Phalle
168,Rachit Srivastava,Kirthika Jayachander,Krishna Yadav,Kavya Mehta
169,Rachit Srivastava,Tvisha Shah,Viren Jadhav,Ketaki Phalle
170,Rachit Srivastava,Tvisha Shah,Kausar Chandra,Riya Shirode
171,Praneel Deshmukh,Tvisha Shah,Avaneesh Mahalle,Kavya Mehta
172,Praneel Deshmukh,Naisha Rastogi,Viren Jadhav,Ketaki Phalle
173,Praneel Deshmukh,Tvisha Shah,Sagnik Ghosh,Ketaki Phalle
174,Praneel Deshmukh,Naisha Rastogi,Kausar Chandra,Trisha Kandpal
175,Praneel Deshmukh,Kirthika Jayachander,Kausar Chandra,Ketaki Phalle
176,Praneel Deshmukh,Tvisha Shah,Kausar Chandra,Sumedha Vaidya
177,Praneel Deshmukh,Tvisha Shah,Sagnik Ghosh,Ketaki Phalle
178,Praneel Deshmukh,Tvisha Shah,Viren Jadhav,Riya Shirode
179,Praneel Deshmukh,Gauravi Zade,Viren Jadhav,Riya Shirode
180,Aadityaraje Desai,Naisha Rastogi,Krishna Yadav,Trisha Kandpal
181,Aadityaraje Desai,Naisha Rastogi,Kausar Chandra,Trisha Kandpal
182,Abhichandra Charke,Naisha Rastogi,Kausar Chandra,Trisha Kandpal
183,Aadityaraje Desai,Naisha Rastogi,Krishna Yadav,Kavya Mehta
184,Aadityaraje Desai,Gauravi Zade,Krishna Yadav,Trisha Kandpal
185,Praneel Deshmukh,Naisha Rastogi,Avaneesh Mahalle,Trisha Kandpal
186,Abhichandra Charke,Naisha Rastogi,Kausar Chandra,Trisha Kandpal
187,Abhichandra Charke,Naisha Rastogi,Krishna Yadav,Ketaki Phalle
188,Abhichandra Charke,Naisha Rastogi,Sagnik Ghosh,Kavya Mehta
189,Aadityaraje Desai,Gauravi Zade,Kausar Chandra,Trisha Kandpal
190,Aadityaraje Desai,Tvisha Shah,Kausar Chandra,Ketaki Phalle
191,Aadityaraje Desai,Gauravi Zade,Krishna Yadav,Sumedha Vaidya
192,Aadityaraje Desai,Gauravi Zade,Viren Jadhav,Trisha Kandpal
193,Aadityaraje Desai,Gauravi Zade,Krishna Yadav,Trisha Kandpal
194,Rachit Srivastava,Gauravi Zade,Krishna Yadav,Sumedha Vaidya
195,Aadityaraje Desai,Gauravi Zade,Avaneesh Mahalle,Trisha Kandpal
196,Aadityaraje Desai,Gauravi Zade,Viren Jadhav,Trisha Kandpal
197,Aadityaraje Desai,Gauravi Zade,Avaneesh Mahalle,Trisha Kandpal
198,Aadityaraje Desai,Gauravi Zade,Sagnik Ghosh,Trisha Kandpal
199,Praneel Deshmukh,Gauravi Zade,Viren Jadhav,Ketaki Phalle
200,Praneel Deshmukh,Gauravi Zade,Viren Jadhav,Trisha Kandpal
201,Praneel Deshmukh,Gauravi Zade,Kausar Chandra,Trisha Kandpal
202,Praneel Deshmukh,Gauravi Zade,Viren Jadhav,Ketaki Phalle
203,Praneel Deshmukh,Gauravi Zade,Kausar Chandra,Trisha Kandpal
204,Aadityaraje Desai,Gauravi Zade,Viren Jadhav,Trisha Kandpal
205,Praneel Deshmukh,Kirthika Jayachander,Viren Jadhav,Kavya Mehta
206,Aadityaraje Desai,Naisha Rastogi,Avaneesh Mahalle,Ketaki Phalle
207,Praneel Deshmukh,Gauravi Zade,Avaneesh Mahalle,Kavya Mehta
208,Aadityaraje Desai,Gauravi Zade,Krishna Yadav,Ketaki Phalle
209,Aadityaraje Desai,Gauravi Zade,Krishna Yadav,Ketaki Phalle
210,Aadityaraje Desai,Gauravi Zade,Kausar Chandra,Ketaki Phalle
211,Aadityaraje Desai,Gauravi Zade,Avaneesh Mahalle,Ketaki Phalle
212,Praneel Deshmukh,Kirthika Jayachander,Kausar Chandra,Trisha Kandpal
213,Praneel Deshmukh,Gauravi Zade,Krishna Yadav,Trisha Kandpal
214,Aadityaraje Desai,Gauravi Zade,Kausar Chandra,Trisha Kandpal
215,Aadityaraje Desai,Gauravi Zade,Kausar Chandra,Ketaki Phalle
216,Aadityaraje Desai,Gauravi Zade,Kausar Chandra,Ketaki Phalle
217,Aadityaraje Desai,Gauravi Zade,Kausar Chandra,Trisha Kandpal
218,Abhichandra Charke,Tvisha Shah,Viren Jadhav,Ketaki Phalle
219,Aadityaraje Desai,Gauravi Zade,Avaneesh Mahalle,Ketaki Phalle
220,Abhichandra Charke,Gauravi Zade,Krishna Yadav,Ketaki Phalle
221,Rachit Srivastava,Gauravi Zade,Kausar Chandra,Trisha Kandpal
222,Praneel Deshmukh,Tvisha Shah,Avaneesh Mahalle,Ketaki Phalle
223,Abhichandra Charke,Gauravi Zade,Viren Jadhav,Ketaki Phalle
224,Abhichandra Charke,Gauravi Zade,Krishna Yadav,Ketaki Phalle
225,Aadityaraje Desai,Gauravi Zade,Kausar Chandra,Trisha Kandpal
226,Aadityaraje Desai,Gauravi Zade,Viren Jadhav,Kavya Mehta
227,Rachit Srivastava,Gauravi Zade,Kausar Chandra,Ketaki Phalle
228,Rachit Srivastava,Gauravi Zade,Kausar Chandra,Ketaki Phalle
229,Abhichandra Charke,Gauravi Zade,Kausar Chandra,Trisha Kandpal
230,Aadityaraje Desai,Gauravi Zade,Kausar Chandra,Ketaki Phalle
231,Praneel Deshmukh,Gauravi Zade,Avaneesh Mahalle,Ketaki Phalle
232,Aadityaraje Desai,Gauravi Zade,Krishna Yadav,Ketaki Phalle
233,Aadityaraje Desai,Naisha Rastogi,Kausar Chandra,Ketaki Phalle
234,Aadityaraje Desai,Gauravi Zade,Avaneesh Mahalle,Ketaki Phalle
235,Aadityaraje Desai,Naisha Rastogi,Kausar Chandra,Ketaki Phalle
236,Aadityaraje Desai,Naisha Rastogi,Viren Jadhav,Ketaki Phalle
237,Aadityaraje Desai,Gauravi Zade,Avaneesh Mahalle,Ketaki Phalle
238,Praneel Deshmukh,Gauravi Zade,Avaneesh Mahalle,Ketaki Phalle
239,Aadityaraje Desai,Gauravi Zade,Sagnik Ghosh,Kavya Mehta
240,Aadityaraje Desai,Gauravi Zade,Krishna Yadav,Ketaki Phalle
241,Praneel Deshmukh,Gauravi Zade,Avaneesh Mahalle,Ketaki Phalle
242,Praneel Deshmukh,Gauravi Zade,Avaneesh Mahalle,Kavya Mehta
243,Aadityaraje Desai,Gauravi Zade,Avaneesh Mahalle,Ketaki Phalle
244,Abhichandra Charke,Gauravi Zade,Avaneesh Mahalle,Ketaki Phalle
245,Praneel Deshmukh,Gauravi Zade,Avaneesh Mahalle,Kavya Mehta
246,Praneel Deshmukh,Gauravi Zade,Avaneesh Mahalle,Ketaki Phalle
247,Aadityaraje Desai,Naisha Rastogi,Krishna Yadav,Kavya Mehta
248,Aadityaraje Desai,Naisha Rastogi,Krishna Yadav,Kavya Mehta
249,Abhichandra Charke,Gauravi Zade,Avaneesh Mahalle,Kavya Mehta
250,Aadityaraje Desai,Naisha Rastogi,Krishna Yadav,Ketaki Phalle
251,Praneel Deshmukh,Gauravi Zade,Krishna Yadav,Kavya Mehta
252,Praneel Deshmukh,Gauravi Zade,Krishna Yadav,Kavya Mehta
253,Aadityaraje Desai,Gauravi Zade,Sagnik Ghosh,Trisha Kandpal
254,Aadityaraje Desai,Gauravi Zade,Krishna Yadav,Ketaki Phalle
255,Rachit Srivastava,Gauravi Zade,Avaneesh Mahalle,Sumedha Vaidya
256,Aadityaraje Desai,Gauravi Zade,Avaneesh Mahalle,Kavya Mehta
257,Praneel Deshmukh,Gauravi Zade,Avaneesh Mahalle,Kavya Mehta
258,Aadityaraje Desai,Gauravi Zade,Avaneesh Mahalle,Kavya Mehta
259,Aadityaraje Desai,Gauravi Zade,Avaneesh Mahalle,Kavya Mehta
260,Aadityaraje Desai,Gauravi Zade,Avaneesh Mahalle,Kavya Mehta
261,Abhichandra Charke,Gauravi Zade,Krishna Yadav,Ketaki Phalle
262,Abhichandra Charke,Naisha Rastogi,Avaneesh Mahalle,Ketaki Phalle
263,Abhichandra Charke,Naisha Rastogi,Avaneesh Mahalle,Ketaki Phalle
264,Praneel Deshmukh,Gauravi Zade,Krishna Yadav,Trisha Kandpal
265,Praneel Deshmukh,Gauravi Zade,Krishna Yadav,Ketaki Phalle
266,Aadityaraje Desai,Tvisha Shah,Viren Jadhav,Trisha Kandpal
267,Aadityaraje Desai,Gauravi Zade,Krishna Yadav,Kavya Mehta
268,Praneel Deshmukh,Gauravi Zade,Avaneesh Mahalle,Ketaki Phalle
269,Abhichandra Charke,Tvisha Shah,Avaneesh Mahalle,Ketaki Phalle
270,Praneel Deshmukh,Gauravi Zade,Kausar Chandra,Kavya Mehta
271,Aadityaraje Desai,Gauravi Zade,Kausar Chandra,Riya Shirode
272,Abhichandra Charke,Naisha Rastogi,Viren Jadhav,Ketaki Phalle
273,Abhichandra Charke,Gauravi Zade,Viren Jadhav,Ketaki Phalle
274,Abhichandra Charke,Tvisha Shah,Sagnik Ghosh,Kavya Mehta
275,Praneel Deshmukh,Gauravi Zade,Krishna Yadav,Kavya Mehta
276,Abhichandra Charke,Gauravi Zade,Krishna Yadav,Ketaki Phalle
277,Praneel Deshmukh,Gauravi Zade,Krishna Yadav,Sumedha Vaidya
278,Aadityaraje Desai,Gauravi Zade,Krishna Yadav,Sumedha Vaidya
279,Aadityaraje Desai,Gauravi Zade,Viren Jadhav,Kavya Mehta
280,Aadityaraje Desai,Gauravi Zade,Avaneesh Mahalle,Ketaki Phalle
281,Praneel Deshmukh,Gauravi Zade,Krishna Yadav,Ketaki Phalle
282,Aadityaraje Desai,Naisha Rastogi,Viren Jadhav,Ketaki Phalle
283,Praneel Deshmukh,Gauravi Zade,Avaneesh Mahalle,Ketaki Phalle
284,Abhichandra Charke,Gauravi Zade,Sagnik Ghosh,Trisha Kandpal
285,Abhichandra Charke,Gauravi Zade,Avaneesh Mahalle,Ketaki Phalle
286,Aadityaraje Desai,Gauravi Zade,Avaneesh Mahalle,Ketaki Phalle
287,Praneel Deshmukh,Gauravi Zade,Avaneesh Mahalle,Ketaki Phalle
288,Praneel Deshmukh,Naisha Rastogi,Avaneesh Mahalle,Kavya Mehta
289,Praneel Deshmukh,Naisha Rastogi,Krishna Yadav,Kavya Mehta
290,Praneel Deshmukh,Gauravi Zade,Viren Jadhav,Trisha Kandpal
291,Aadityaraje Desai,Gauravi Zade,Avaneesh Mahalle,Sumedha Vaidya
292,Aadityaraje Desai,Gauravi Zade,Kausar Chandra,Trisha Kandpal
293,Abhichandra Charke,Tvisha Shah,Krishna Yadav,Kavya Mehta
294,Aadityaraje Desai,Gauravi Zade,Viren Jadhav,Trisha Kandpal
295,Praneel Deshmukh,Gauravi Zade,Avaneesh Mahalle,Kavya Mehta
296,Aadityaraje Desai,Gauravi Zade,Avaneesh Mahalle,Ketaki Phalle
297,Aadityaraje Desai,Gauravi Zade,Krishna Yadav,Kavya Mehta
298,Aadityaraje Desai,Gauravi Zade,Krishna Yadav,Kavya Mehta
299,Abhichandra Charke,Gauravi Zade,Kausar Chandra,Trisha Kandpal
300,Praneel Deshmukh,Naisha Rastogi,Viren Jadhav,Ketaki Phalle
301,Praneel Deshmukh,Gauravi Zade,Avaneesh Mahalle,Kavya Mehta
302,Abhichandra Charke,Gauravi Zade,Avaneesh Mahalle,Kavya Mehta
303,Rachit Srivastava,Gauravi Zade,Avaneesh Mahalle,Kavya Mehta
304,Aadityaraje Desai,Gauravi Zade,Krishna Yadav,Ketaki Phalle
305,Aadityaraje Desai,Gauravi Zade,Kausar Chandra,Kavya Mehta
306,Praneel Deshmukh,Naisha Rastogi,Kausar Chandra,Kavya Mehta
307,Abhichandra Charke,Gauravi Zade,Kausar Chandra,Kavya Mehta
308,Abhichandra Charke,Naisha Rastogi,Avaneesh Mahalle,Kavya Mehta
309,Aadityaraje Desai,Naisha Rastogi,Avaneesh Mahalle,Ketaki Phalle
310,Abhichandra Charke,Naisha Rastogi,Kausar Chandra,Kavya Mehta
311,Praneel Deshmukh,Gauravi Zade,Krishna Yadav,Ketaki Phalle
312,Praneel Deshmukh,Gauravi Zade,Krishna Yadav,Trisha Kandpal
313,Aadityaraje Desai,Gauravi Zade,Viren Jadhav,Kavya Mehta
314,Abhichandra Charke,Gauravi Zade,Kausar Chandra,Trisha Kandpal
315,Abhichandra Charke,Gauravi Zade,Avaneesh Mahalle,Ketaki Phalle
316,Praneel Deshmukh,Gauravi Zade,Avaneesh Mahalle,Ketaki Phalle
317,Praneel Deshmukh,Gauravi Zade,Kausar Chandra,Trisha Kandpal
318,Praneel Deshmukh,Gauravi Zade,Avaneesh Mahalle,Ketaki Phalle
319,Aadityaraje Desai,Gauravi Zade,Viren Jadhav,Kavya Mehta
320,Praneel Deshmukh,Naisha Rastogi,Avaneesh Mahalle,Ketaki Phalle
321,Praneel Deshmukh,Naisha Rastogi,Avaneesh Mahalle,Kavya Mehta
322,Praneel Deshmukh,Gauravi Zade,Krishna Yadav,Kavya Mehta
323,Praneel Deshmukh,Gauravi Zade,Krishna Yadav,Kavya Mehta
324,Praneel Deshmukh,Gauravi Zade,Viren Jadhav,Trisha Kandpal
325,Praneel Deshmukh,Gauravi Zade,Kausar Chandra,Kavya Mehta
326,Aadityaraje Desai,Naisha Rastogi,Viren Jadhav,Ketaki Phalle
327,Aadityaraje Desai,Gauravi Zade,Krishna Yadav,Ketaki Phalle
328,Aadityaraje Desai,Gauravi Zade,Avaneesh Mahalle,Kavya Mehta
329,Praneel Deshmukh,Naisha Rastogi,Avaneesh Mahalle,Kavya Mehta
330,Praneel Deshmukh,Naisha Rastogi,Avaneesh Mahalle,Kavya Mehta
331,Aadityaraje Desai,Gauravi Zade,Kausar Chandra,Ketaki Phalle
332,Praneel Deshmukh,Gauravi Zade,Kausar Chandra,Ketaki Phalle
333,Praneel Deshmukh,Gauravi Zade,Krishna Yadav,Ketaki Phalle
334,Aadityaraje Desai,Gauravi Zade,Viren Jadhav,Sumedha Vaidya
335,Aadityaraje Desai,Gauravi Zade,Sagnik Ghosh,Sumedha Vaidya
336,Aadityaraje Desai,Gauravi Zade,Viren Jadhav,Trisha Kandpal
337,Aadityaraje Desai,Kirthika Jayachander,Kausar Chandra,Trisha Kandpal
338,Praneel Deshmukh,Gauravi Zade,Krishna Yadav,Trisha Kandpal
339,Aadityaraje Desai,Gauravi Zade,Krishna Yadav,Trisha Kandpal
340,Aadityaraje Desai,Gauravi Zade,Krishna Yadav,Ketaki Phalle
341,Aadityaraje Desai,Gauravi Zade,Viren Jadhav,Kavya Mehta
342,Praneel Deshmukh,Gauravi Zade,Krishna Yadav,Ketaki Phalle
343,Praneel Deshmukh,Gauravi Zade,Viren Jadhav,Ketaki Phalle
344,Aadityaraje Desai,Gauravi Zade,Viren Jadhav,Ketaki Phalle
345,Praneel Deshmukh,Tvisha Shah,Avaneesh Mahalle,Ketaki Phalle
346,Aadityaraje Desai,Tvisha Shah,Krishna Yadav,Trisha Kandpal
347,Praneel Deshmukh,Gauravi Zade,Krishna Yadav,Ketaki Phalle
348,Aadityaraje Desai,Gauravi Zade,Krishna Yadav,Kavya Mehta
349,Abhichandra Charke,Gauravi Zade,Krishna Yadav,Ketaki Phalle
350,Praneel Deshmukh,Naisha Rastogi,Viren Jadhav,Kavya Mehta
351,Praneel Deshmukh,Gauravi Zade,Krishna Yadav,Riya Shirode
352,Abhichandra Charke,Gauravi Zade,Viren Jadhav,Kavya Mehta
353,Praneel Deshmukh,Gauravi Zade,Viren Jadhav,Kavya Mehta
354,Abhichandra Charke,Gauravi Zade,Avaneesh Mahalle,Sumedha Vaidya
355,Praneel Deshmukh,Gauravi Zade,Sagnik Ghosh,Kavya Mehta
356,Aadityaraje Desai,Naisha Rastogi,Krishna Yadav,Kavya Mehta
357,Praneel Deshmukh,Gauravi Zade,Krishna Yadav,Kavya Mehta
358,Aadityaraje Desai,Gauravi Zade,Viren Jadhav,Trisha Kandpal
359,Praneel Deshmukh,Gauravi Zade,Avaneesh Mahalle,Ketaki Phalle
360,Rachit Srivastava,Kirthika Jayachander,Sagnik Ghosh,Riya Shirode
361,Aadityaraje Desai,Gauravi Zade,Kausar Chandra,Sumedha Vaidya
362,Aadityaraje Desai,Gauravi Zade,Kausar Chandra,Sumedha Vaidya
363,Praneel Deshmukh,Tvisha Shah,Sagnik Ghosh,Trisha Kandpal
364,Aadityaraje Desai,Tvisha Shah,Viren Jadhav,Trisha Kandpal
365,Aadityaraje Desai,Tvisha Shah,Viren Jadhav,Ketaki Phalle
366,Rachit Srivastava,Gauravi Zade,Avaneesh Mahalle,Trisha Kandpal
367,Abhichandra Charke,Naisha Rastogi,Avaneesh Mahalle,Ketaki Phalle
368,Aadityaraje Desai,Kirthika Jayachander,Avaneesh Mahalle,Ketaki Phalle
369,Abhichandra Charke,Naisha Rastogi,Viren Jadhav,Ketaki Phalle
370,Praneel Deshmukh,Tvisha Shah,Kausar Chandra,Sumedha Vaidya
371,Praneel Deshmukh,Gauravi Zade,Avaneesh Mahalle,Ketaki Phalle
372,Aadityaraje Desai,Tvisha Shah,Krishna Yadav,Ketaki Phalle
373,Abhichandra Charke,Gauravi Zade,Kausar Chandra,Ketaki Phalle
374,Abhichandra Charke,Gauravi Zade,Kausar Chandra,Ketaki Phalle
375,Abhichandra Charke,Gauravi Zade,Kausar Chandra,Ketaki Phalle
376,Praneel Deshmukh,Gauravi Zade,Kausar Chandra,Ketaki Phalle
377,Abhichandra Charke,Gauravi Zade,Sagnik Ghosh,Sumedha Vaidya
378,Praneel Deshmukh,Gauravi Zade,Avaneesh Mahalle,Ketaki Phalle
379,Aadityaraje Desai,Naisha Rastogi,Avaneesh Mahalle,Trisha Kandpal
380,Aadityaraje Desai,Naisha Rastogi,Avaneesh Mahalle,Trisha Kandpal
381,Praneel Deshmukh,Gauravi Zade,Avaneesh Mahalle,Kavya Mehta
382,Aadityaraje Desai,Tvisha Shah,Krishna Yadav,Kavya Mehta
383,Aadityaraje Desai,Kirthika Jayachander,Sagnik Ghosh,Sumedha Vaidya
384,Praneel Deshmukh,Tvisha Shah,Viren Jadhav,Riya Shirode