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"source": [
"# DS 122 Coding Quiz\n",
"\n",
"**Total: 75 points**\n",
"\n",
"**Name:**\n",
"\n",
"**BUID:**"
],
"metadata": {
"id": "LXEd_Tscon5g"
}
},
{
"cell_type": "markdown",
"source": [
"## Instructions\n",
"\n",
"The assessment consists of 2 problems:\n",
"\n",
"1. Problem 1 - Data Scientist at Instagram [25 points]\n",
"2. Problem 2 - Ferrari vs Mercedes [50 points]\n",
"\n",
"- The problems are **not** related to one another, so you can begin working on any problem that you want first.\n",
"\n",
"- Every problem has clear instructions and some starter code. Please answer every question after reading the instructions clearly.\n",
"\n",
"- After you finish your code, please remember to **run all the cells**.\n",
"\n",
"- Upload your submission in the form of an **.ipynb** file and a **pdf** file. The easiest way to generate a pdf is to use `command + P` on Mac or `ctrl + P` on Windows.\n",
"\n",
"- It is highly recommended to run the code on Colab for ease."
],
"metadata": {
"id": "AQi6-eNmGhmw"
}
},
{
"cell_type": "markdown",
"source": [
"## Problem 1 - Data Scientist at Instagram [25 points]\n",
"\n",
"- You are a data scientist at Instagram.\n",
"- Instagram has 3 different types of accounts:\n",
" 1. private\n",
" 2. creater\n",
" 3. business\n",
"\n",
"- Users of these 3 types of account give the app a rating on App store.\n",
"- Ratings are between 1 and 5."
],
"metadata": {
"id": "O6klb9T7KC9B"
}
},
{
"cell_type": "code",
"source": [
"#imports\n",
"import pandas as pd\n",
"import numpy as np\n",
"from scipy.stats import ttest_1samp"
],
"metadata": {
"id": "e8rJvsr-KDet"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"**Task 0: Loading [0 points]**\n",
"\n",
"Load the CSV file called `ig_ratings_random.csv` below into a pandas dataframe.\n",
"\n",
"The easiest way to load the file would be to, in the code below, replace `path_to_file` with the path to the file stored on your device or Colab session."
],
"metadata": {
"id": "ARKR6oYVMuu4"
}
},
{
"cell_type": "code",
"source": [
"#loading the data\n",
"df = pd.read_csv('/content/ig_ratings_random.csv')"
],
"metadata": {
"id": "qwXZpP_WM1gD"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"df.head()"
],
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"colab": {
"base_uri": "https://localhost:8080/",
"height": 206
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"id": "hF36Pvt-NTUz",
"outputId": "7ea344f3-5a3c-4202-d87a-0119fdc7524f"
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"execution_count": null,
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{
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" User Category Satisfaction Score\n",
"0 Private 2\n",
"1 Private 3\n",
"2 Private 1\n",
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"metadata": {},
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]
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{
"cell_type": "code",
"source": [
"#creating numpy arrays for each category\n",
"private_scores = df[df['User Category'] == 'Private']['Satisfaction Score'].to_numpy()\n",
"creator_scores = df[df['User Category'] == 'Premium Creator']['Satisfaction Score'].to_numpy()\n",
"business_scores = df[df['User Category'] == 'Business']['Satisfaction Score'].to_numpy()"
],
"metadata": {
"id": "uA0NeOSkyaI7"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"**Task 1: Complete the code below: [10 points]**\n",
"\n",
"- Create samples from the above defined ratings.\n",
"- Use simple sampling without replacement.\n",
"- Concatenate the 3 arrays created."
],
"metadata": {
"id": "3tIdeaimLJg5"
}
},
{
"cell_type": "code",
"source": [
"#create samples without replacement\n",
"private_sample = np.random.choice(private_scores, 50, replace=False)\n",
"creator_sample = np.random.choice(creator_scores, 30, replace=False)\n",
"business_sample = np.random.choice(business_scores, 20, replace=False)"
],
"metadata": {
"id": "_WJE7I__LVE7"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"**QUESTION**\n",
"\n",
"Justify your reasoning behind the number of samples picked."
],
"metadata": {
"id": "TDO-EgMP1GnC"
}
},
{
"cell_type": "code",
"source": [
"#concatenate samples\n",
"sample = np.concatenate([private_sample, creator_sample, business_sample])"
],
"metadata": {
"id": "pZ5rfHpHLsPS"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"#RUN THIS CELL\n",
"#calculating mean and std dev\n",
"sample_mean = np.mean(sample)\n",
"sample_std_dev = np.std(sample)\n",
"\n",
"#printing\n",
"print(\"Sample Mean:\", sample_mean)\n",
"print(\"Standard Deviation:\", sample_std_dev)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "2fDJmyOgL0it",
"outputId": "58e5e77f-0fde-4b50-d4cf-09b9d9887df8"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Sample Mean: 3.44\n",
"Standard Deviation: 1.33656275572829\n"
]
}
]
},
{
"cell_type": "markdown",
"source": [
"**Task 2: Hypothesis testing [15 points]**\n",
"\n",
"Check whether the average satisfaction score for `Creators` is significantly different from `3.5`.\n",
"\n",
"To do this, complete the 2 incomplete parts of the code below."
],
"metadata": {
"id": "UGCii8RpNvvN"
}
},
{
"cell_type": "code",
"source": [
"#converting column to array\n",
"user_satisfaction = df['Satisfaction Score'].to_numpy()"
],
"metadata": {
"id": "UTimqq53zKhi"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"# Part a - Find out the mean of user satisfaction scores\n",
"satisfaction_mean = np.mean(user_satisfaction)\n",
"print(\"Mean User Satisfaction Score:\", satisfaction_mean)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "tATyyG-fzRYW",
"outputId": "6c68cf7c-702b-4af7-d8fa-75255b14343c"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Mean User Satisfaction Score: 3.614\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"# Part b - Run a t-test to check whether the average satisfaction score is significantly different from 3.5\n",
"test_stat, p_value = ttest_1samp(user_satisfaction, 3.5)\n",
"print(\"p-value:\", p_value)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "j64vI-4_zTDK",
"outputId": "e5f67fb6-d730-467e-f434-3fad99b7d25c"
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"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"p-value: 0.004200912315335384\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"if p_value < 0.05:\n",
" print(\"Reject null hypothesis: Average satisfaction is significantly different from 3.5\")\n",
"else:\n",
" print(\"Fail to reject null hypothesis: Average satisfaction is not significantly different from 3.5\")"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "AAzH9HrkzU7V",
"outputId": "8805a58c-cf54-43ab-9a3b-1e347d2c3eb3"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Reject null hypothesis: Average satisfaction is significantly different from 3.5\n"
]
}
]
},
{
"cell_type": "markdown",
"source": [
"### Problem 2 - Ferrari vs Mercedes\n",
"\n",
"- You come across a dataset containing the points given to a team after completing 30 F1 races by two teams called `Ferrari` and `Mercedes`.\n",
"- For example, `Ferrari` with time `5` would mean that Ferrari won by 5 seconds and recieved 5 points for that win.\n",
"- Your task is to determine the likelihood of `Mercedes` winning against `Ferrari` based on their scoring patterns."
],
"metadata": {
"id": "QAZOi1BY3dCF"
}
},
{
"cell_type": "code",
"source": [
"#imports\n",
"import pandas as pd\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"from scipy.stats import poisson"
],
"metadata": {
"id": "xc2hJekF5yNt"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"**Task 0: Load the csv file**"
],
"metadata": {
"id": "z7XoLtX6-ESY"
}
},
{
"cell_type": "code",
"source": [
"#opening\n",
"df = pd.read_csv('/content/f1_data.csv')\n",
"df.head()"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 206
},
"id": "qvJ1AOmC68Eq",
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"output_type": "execute_result",
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" Team Time taken\n",
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"1 Ferrari 7\n",
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"\n",
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"\n",
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"\n",
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"\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-293d0150-4392-4a5b-8359-96151493e6bc 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-293d0150-4392-4a5b-8359-96151493e6bc');\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",
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" }\n",
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]
},
"metadata": {},
"execution_count": 64
}
]
},
{
"cell_type": "code",
"source": [
"#preprocessing the data\n",
"df = df[df['Team'].isin(['Ferrari', 'Mercedes'])]"
],
"metadata": {
"id": "Xp3C0wxJ8KXk"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"**Task 1 - Calculate the average time taken by both teams.**"
],
"metadata": {
"id": "lamV3EC9-I5V"
}
},
{
"cell_type": "code",
"source": [
"# 1. Calculate the average time taken\n",
"ferrari_avg = df[df['Team'] == 'Ferrari']['Time taken'].mean()\n",
"mercedes_avg = df[df['Team'] == 'Mercedes']['Time taken'].mean()"
],
"metadata": {
"id": "UQGVGhud8Zp9"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"**Task 2 - Fit distributions for Ferrari and Mercedes.**\n",
"\n",
"Fit a distribution for Ferrari. In other words, calculate the probabiliy for times 0 to 15 according to the poisson PMF."
],
"metadata": {
"id": "JFoS28u1-SGL"
}
},
{
"cell_type": "code",
"source": [
"#2a. Fit a distribution for Ferrari\n",
"ferrari_poisson = [poisson.pmf(time, ferrari_avg) for time in range(15)]\n",
"\n",
"#2b. Fit a distribution for Mercedes\n",
"mercedes_poisson = [poisson.pmf(time, mercedes_avg) for time in range(15)]"
],
"metadata": {
"id": "c69o4XjO8a-O"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"**Task 3 - Define likelihood and update functions.**"
],
"metadata": {
"id": "LFhYo9Ut-eZU"
}
},
{
"cell_type": "code",
"source": [
"#3a. Define likelihood functions\n",
"def likelihood_poisson(lam, data):\n",
" return [poisson.pmf(data, l) for l in lam]"
],
"metadata": {
"id": "mMBws3XS8hZF"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"#3b. Define update function\n",
"def update(distribution, likelihood):\n",
" distribution['probs'] = distribution['probs'] * likelihood\n",
" prob_data = distribution['probs'].sum()\n",
" distribution['probs'] = distribution['probs'] / prob_data\n",
" return distribution"
],
"metadata": {
"id": "rfcyjuRc-oZ5"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"**Task 4 - Update for Ferrari**"
],
"metadata": {
"id": "2gjFonnm-s7j"
}
},
{
"cell_type": "code",
"source": [
"# 6. Update for Ferrari\n",
"prior_ferrari = pd.DataFrame({'lams': np.arange(0, 15), 'probs': ferrari_poisson})\n",
"ferrari = prior_ferrari.copy()\n",
"update(ferrari, likelihood_poisson(ferrari['lams'], 9))"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 520
},
"id": "qqBTDCk08ivk",
"outputId": "0e0e6c8e-7c21-4542-f7fe-8f072475eb01"
},
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
" lams probs\n",
"0 0 0.000000e+00\n",
"1 1 3.415740e-08\n",
"2 2 2.541309e-05\n",
"3 3 9.464334e-04\n",
"4 4 9.158225e-03\n",
"5 5 3.966104e-02\n",
"6 6 9.912350e-02\n",
"7 7 1.647905e-01\n",
"8 8 1.991143e-01\n",
"9 9 1.855946e-01\n",
"10 10 1.392244e-01\n",
"11 11 8.673400e-02\n",
"12 12 4.596611e-02\n",
"13 13 2.111961e-02\n",
"14 14 8.541865e-03"
],
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" quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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]
},
"metadata": {},
"execution_count": 82
}
]
},
{
"cell_type": "markdown",
"source": [
"**Task 5 - Update for Mercedes**"
],
"metadata": {
"id": "lsj9n08p-1TP"
}
},
{
"cell_type": "code",
"source": [
"#5. Update for Mercedes\n",
"prior_mercedes = pd.DataFrame({'lams': np.arange(0, 15), 'probs': mercedes_poisson})\n",
"mercedes = prior_mercedes.copy()\n",
"update(mercedes, likelihood_poisson(mercedes['lams'], 7))"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 520
},
"id": "XuMjNyPm8l-Y",
"outputId": "86a34b5b-0ccb-4591-8f05-65cd006bc121"
},
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
" lams probs\n",
"0 0 0.000000\n",
"1 1 0.000059\n",
"2 2 0.006057\n",
"3 3 0.054988\n",
"4 4 0.164176\n",
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"6 6 0.237619\n",
"7 7 0.159198\n",
"8 8 0.080783\n",
"9 9 0.032634\n",
"10 10 0.010877\n",
"11 11 0.003072\n",
"12 12 0.000750\n",
"13 13 0.000161\n",
"14 14 0.000031"
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]
},
"metadata": {},
"execution_count": 83
}
]
},
{
"cell_type": "markdown",
"source": [
"**Task 6 - Calculate the probability of Ferrari being faster**"
],
"metadata": {
"id": "UcUipTE9-4xP"
}
},
{
"cell_type": "code",
"source": [
"#6. Calculate the probability of Ferrari being faster\n",
"def prob_of_ferrari_being_faster(dist_ferrari, dist_mercedes):\n",
" total = 0\n",
" for index_f, row_f in dist_ferrari.iterrows():\n",
" for index_m, row_m in dist_mercedes.iterrows():\n",
" if row_f['lams'] < row_m['lams']:\n",
" total += row_f['probs'] * row_m['probs']\n",
" return total"
],
"metadata": {
"id": "xwoZi8pj8oG3"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"probability_ferrari_faster = prob_of_ferrari_being_faster(ferrari, mercedes)\n",
"print(\"Probability of Ferrari being faster:\", probability_ferrari_faster)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "LUbPZdpo8pW-",
"outputId": "52e62576-043b-40a6-f645-132289638b66"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Probability of Ferrari being faster: 0.09135514896649938\n"
]
}
]
},
{
"cell_type": "markdown",
"source": [
"**Task 7 - Simulate another race**"
],
"metadata": {
"id": "T6fsxNho-82A"
}
},
{
"cell_type": "code",
"source": [
"# 7. Simulate another race\n",
"pmf_table_m = pd.DataFrame([[poisson.pmf(time, lam) for time in range(15)] for lam in prior_mercedes['lams']])\n",
"pmf_table_m = (pmf_table_m.T / pmf_table_m.T.sum()).T"
],
"metadata": {
"id": "nmMPLkyk8rfo"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"pred_f = (pmf_table_m.transpose() * ferrari['probs']).sum(axis=1)\n",
"pred_m = (pmf_table_m.transpose() * mercedes['probs']).sum(axis=1)"
],
"metadata": {
"id": "Z4n7ZWEk8sGA"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"plt.plot(range(15), pred_f, color=\"blue\")\n",
"plt.plot(range(15), pred_m, color=\"red\")\n",
"plt.legend(['Ferrari', 'Mercedes'], fontsize=14, loc='best')\n",
"plt.xlabel('Time taken', size=16)\n",
"plt.ylabel('Probability', size=16)\n",
"plt.show()"
],
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"colab": {
"base_uri": "https://localhost:8080/",
"height": 455
},
"id": "uRVcYEYL8JQK",
"outputId": "17b29e23-96cc-41f8-dd61-a73abf79ef30"
},
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
],
"image/png": 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\n"
},
"metadata": {}
}
]
}
]
}